A Reliability Assessment of the Basic Erosive Wear Examination and the Tooth Wear Evaluation System 2.0 Utilizing Intraoral Scan Data

A Reliability Assessment of the Basic Erosive Wear Examination and the Tooth Wear Evaluation System 2.0 Utilizing Intraoral Scan Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Reliability Assessment of the Basic Erosive Wear Examination and the Tooth Wear Evaluation System 2.0 Utilizing Intraoral Scan Data Maria Lorens, Iwona Tomaszewska This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8502313/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives This study assessed the reliability and clinical applicability of two tooth wear screening indices—Basic Erosive Wear Examination (BEWE) and the Tooth Wear Screening module of the Tooth Wear Evaluation System 2.0 (TWES 2.0)—using intraoral scans. Materials and methods A total of 246 anonymized intraoral scans from adult patients were independently evaluated by two calibrated examiners. Examiners calibration was performed prior to the study using a representative set of intraoral scans and a reference standard. Calibration was repeated until a predefined level of agreement was achieved before formal data collection. Scores for all sextants were recorded for BEWE and TWES 2.0. Inter-rater agreement was assessed using Wilcoxon signed-rank tests to detect systematic differences between paired scores, intraclass correlation coefficients (ICCs) to assess inter-examiner consistency, Bland–Altman plots to evaluate agreement between examiners. Statistical significance was set at p < 0.05. Results BEWE demonstrated good reliability, with ICCs ranging from 0.761 to 0.852 across sextants. According to commonly used ICC interpretation thresholds (poor 0.90), the observed ICCs indicate good inter-examiner reliability. Bland–Altman analysis showed small differences between examiners and no systematic bias. TWES 2.0 exhibited moderate to good reliability, with ICCs between 0.543 and 0.761. Conclusions Both BEWE and TWES 2.0 are reliable and practical for screening noncarious tooth wear via intraoral scans. BEWE showed slightly higher inter-rater consistency, whereas TWES 2.0 allows more detailed evaluation of occlusal and palatal surfaces. These indices can support standardized monitoring, early detection, and clinical management of tooth wear. Examiner calibration remains essential, particularly for TWES 2.0. Clinical Significance: Tooth wear is an increasingly prevalent condition in modern dentistry, often progressing silently until advanced stages. The application of BEWE and TWES 2.0 to intraoral scans provides clinicians with a standardized, noninvasive, and reproducible method for detecting and monitoring tooth wear at an early stage. Integrating these indices into routine digital workflows supports timely diagnosis, preventive management, and long-term follow-up of patients affected by erosive and attritional tooth wear. Clinical trial registration This study did not involve an interventional clinical trial and therefore was not registered in a clinical trial registry. The study protocol was reviewed and approved by the Bioethics Committee at the District Medical Chamber in Kraków, Poland (approval number: L.dz.OIL/KBL/22/2025, issued on 27 May 2025) Attrition Basic Erosive Wear Examination (BEWE) Erosion Tooth Wear Tooth Wear Evaluation System 2.0 (TWES 2.0) Tooth Wear Index (TWI) Intraoral Scans Inter-Rater Reliability 1 Introduction Tooth wear is a multifactorial condition characterized by the non-carious loss of dental hard tissues. It results from three principal mechanisms—erosion, attrition, and abrasion—which may act independently or synergistically [ 1 , 2 ], with erosion often considered the predominant factor [ 3 ]. As an irreversible process, tooth wear progressively compromises enamel and dentin, leading to clinical problems such as dentin hypersensitivity, loss of vertical dimension, impaired esthetics [ 4 ]. It is also increasingly recognized as a clinically relevant condition requiring early identification and monitoring [ 5 ]. The prevalence of tooth wear has increased worldwide and is now frequently observed across all age groups, with a particularly concerning rise among younger patients [ 6 – 8 ]. Epidemiological studies report a wide prevalence range of tooth wear in adult populations, varying from approximately 20% to over 50%, depending on age, diagnostic criteria, and assessment method. While early-stage tooth wear is often asymptomatic, moderate to severe forms have been associated with functional impairments, including loss of occlusal vertical dimension, compromised masticatory efficiency, dentin hypersensitivity, and increased need for complex restorative interventions. Importantly, several studies indicate that a substantial proportion of younger adults already exhibit early signs of erosive and attritional wear, underscoring the clinical relevance of early detection and preventive screening strategies. In this context, the use of standardized, reproducible screening indices is essential to enable timely diagnosis, risk stratification, and monitoring of disease progression [ 4 , 6 , 8 ]. Lifestyle- and diet-related factors, parafunctional habits, and demographic influences contribute to its multifactorial etiology. This growing burden highlights the need for reliable diagnostic and monitoring methods that can be applied effectively in everyday clinical practice [ 9 ]. To promote standardization in the assessment of tooth wear, the 2017 European Consensus Statement recommended three principal indices: the Basic Erosive Wear Examination (BEWE), the Tooth Wear Index (TWI), and the Tooth Wear Evaluation System 2.0 (TWES 2.0) [ 10 ]. These tools were identified as valid frameworks for recording the severity and distribution of wear. However, their clinical usefulness differs. Although the Tooth Wear Index (TWI) remains a valuable reference for detailed epidemiological and research purposes, its extensive scoring system and time-consuming application limit its feasibility for rapid screening and routine clinical use [ 11 ]. For this reason, the present study focused exclusively on BEWE and the Tooth Wear Screening Module of TWES 2.0, which are designed for efficient clinical screening [ 12 – 15 ]. By contrast, the BEWE and TWES 2.0 include simplified scoring systems designed to facilitate rapid chairside assessment, making them more practical as screening tools [ 13 , 14 ]. In particular, the Tooth Wear Screening component of TWES 2.0 is especially well-suited for initial examinations, providing a quick overview of tooth wear status that can efficiently guide further diagnostic or preventive steps. While both BEWE and TWES 2.0 are recommended for clinical assessment of tooth wear, they differ in scope and level of detail. BEWE is a simplified screening index primarily focused on erosive tooth wear, offering high feasibility and ease of use in routine clinical practice, but providing limited information on wear distribution and etiology. In contrast, TWES 2.0 adopts a broader conceptual framework that encompasses erosion, attrition, and abrasion, allowing for a more detailed surface-specific assessment, particularly on occlusal and palatal surfaces. This increased diagnostic detail, however, may come at the cost of greater examiner dependence and potentially reduced reproducibility. Comparing these indices when applied to intraoral scans may therefore provide valuable insights into their relative reliability and practical applicability in digital workflows. Although several indices are available for assessing tooth wear, BEWE and the Tooth Wear Screening Module of TWES 2.0 were selected for this study due to their feasibility for routine clinical use and compatibility with digital workflows. Both indices are designed for rapid screening and rely on simplified, ordinal scoring systems that can be readily applied to intraoral scans. In contrast, the Tooth Wear Index (TWI), while valuable for detailed epidemiological assessment, requires extensive surface-level scoring and is less suited to time-efficient screening or digital assessment. Previous studies have also highlighted the practical advantages of BEWE and TWES 2.0 when applied in a digital context, supporting their use as screening tools in contemporary dental practice [ 15 ]. With the increasing integration of intraoral scanners in dentistry, high-resolution three-dimensional digital models of dentitions offer novel opportunities for tooth wear assessment using digital models [ 16 , 17 ]. This development raises the question of whether screening indices such as BEWE and TWES 2.0 can be reliably applied in a digital environment and, importantly, how clinically useful they are for early detection, monitoring, and treatment planning. Previous studies have evaluated tooth wear indices using clinical examinations, dental casts, and, in selected reports, digital models [ 15 , 17 , 18 ]. However, available evidence remains heterogeneous, and data on the inter-rater reliability of BEWE and TWES 2.0 when applied exclusively to intraoral scan–derived STL files are still limited. In particular, it remains unclear whether these screening indices maintain consistent examiner agreement when assessments rely solely on three-dimensional digital models rather than direct clinical inspection [ 17 , 18 ]. Given the increasing use of intraoral scanners in routine dental workflows, establishing the reproducibility of BEWE and TWES 2.0 in a fully digital context is clinically meaningful. The objective of this study was to evaluate and compare the inter-rater reliability of the Basic Erosive Wear Examination (BEWE) and the Tooth Wear Screening Module of the Tooth Wear Evaluation System 2.0 (TWES 2.0) when applied to intraoral scan–derived digital models. Specifically, the study aimed to assess examiner agreement across sextants using standardized reliability metrics. The null hypothesis was that no significant differences would exist between the scores assigned by the two examiners for either BEWE or TWES 2.0 when applied to intraoral scans, and that both indices would demonstrate comparable inter-rater reliability. 2 Materials and methods 2.1 Study design and ethical considerations This retrospective study analyzed anonymized intraoral scans to evaluate the reliability and clinical applicability of two tooth wear screening indices: the Basic Erosive Wear Examination (BEWE) and the Tooth Wear Screening module of the Tooth Wear Evaluation System 2.0 (TWES 2.0). The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Bioethics Committee at the District Medical Chamber in Kraków, Poland (approval number: L.dz.OIL/KBL/22/2025, issued on 27 May 2025). Due to the retrospective and anonymized nature of the data, individual patient consent was not required; written permission to use the scans was obtained from all three participating dental clinics. This study evaluated whether the BEWE and the TWES 2.0 Screening Module can be applied consistently to intraoral scan–derived digital models. Because no chairside clinical examinations were available, the analysis focused on inter-rater reliability rather than validation against a clinical reference standard. 2.2 Description of indices 2.2.1 BEWE The Basic Erosive Wear Examination (BEWE), is a standardized and widely applied screening tool for erosive tooth wear [ 12 , 19 – 21 ]. The dentition is divided into sextants, and in each sextant the surface with the most severe lesion is recorded. Third molars are excluded. The sextants were defined as follows: Sextant 1 (maxillary right posterior: teeth 14–17), Sextant 2 (maxillary anterior: teeth 13–23), Sextant 3 (maxillary left posterior: teeth 24–27), Sextant 4 (mandibular left posterior: teeth 34–37), Sextant 5 (mandibular anterior: teeth 33–43), and Sextant 6 (mandibular right posterior: teeth 44–47). Surfaces are scored on a four-point ordinal scale according to the following criteria: Score 0 – No evidence of erosive tooth wear. Score 1 – Initial loss of surface texture, with early enamel softening but without distinct tissue loss. Score 2 – Distinct hard tissue loss involving less than 50% of the surface area. Score 3 – Hard tissue loss involving more than 50% of the surface area. The cumulative BEWE score is obtained by summing the sextant scores across the full dentition. This sum can then be used to stratify patients into risk categories (none, low, medium, or high risk), which are linked to clinical recommendations for monitoring and management. The system is designed to be simple, reproducible, and feasible for rapid chairside application, while still allowing clinicians to make informed decisions about preventive or restorative needs. For the purposes of the present study, however, BEWE scores were recorded and analyzed separately for each sextant, with each sextant constituting an individual unit of analysis; the cumulative BEWE score was not used for statistical comparisons. 2.2.2 Tooth wear evaluation system 2.0 (TWES 2.0) The Tooth Wear Evaluation System 2.0 (TWES 2.0) is a standardized framework for assessing non-carious tooth wear caused by erosion, attrition, and abrasion [ 13 , 14 ]. TWES 2.0 consists of two modules: the Tooth Wear Screening Module, intended for rapid clinical or epidemiological assessments, and the Tooth Wear Status Module, which allows a more detailed evaluation for treatment planning and comprehensive documentation. While the Status module enables extended analysis of individual surfaces and severity, in this study we focused exclusively on the Screening Module, which facilitates quick, reliable assessment in routine practice and large-scale studies [ 14 ]. In the Tooth Wear Screening Module, the dentition is divided into sextants, and the most severely affected surface within each sextant is recorded. Palatal surfaces in Sextant 2 receive additional evaluation to ensure thorough assessment of this often-affected area. Scoring uses two separate five-point ordinal scales: 1. Occlusal and incisal surfaces: Grade 0: No visible wear Grade 1: Wear limited to enamel Grade 2: Exposed dentin with ≤ 1/3 loss of clinical crown height Grade 3: Loss of crown height > 1/3 but < 2/3 Grade 4: Loss of crown height ≥ 2/3 2. Non-occlusal surfaces (buccal and lingual): Grade 0: No visible wear Grade 1: Wear limited to enamel Grade 2: Exposed dentin affecting < 50% of the surface Grade 3: Exposed dentin affecting ≥ 50% of the surface Grade 4: Complete enamel loss or pulp exposure Scores are recorded independently for each sextant, without summing across sextants. Clinical recommendations are guided by severity: Grades 0–1 indicate routine monitoring, Grade 2 may prompt further evaluation, and Grades 3–4 require a comprehensive Status module assessment. This modular approach ensures that the Screening Module remains efficient and practical, while the Status module can be applied when more detailed clinical assessment is needed. Accordingly, in this study, each sextant score constituted a separate data point for statistical analysis. 2.3 Data collection and examiner calibration A total of 246 intraoral scans from unique adult patients (≥ 18 years) were included in the study. Scans were acquired in two clinics using the Medit i500 scanner (Medit Corp., Seoul, South Korea) and in one clinic using the iTero Element 2 scanner (Align Technology, San Jose, CA, USA). Intraoral scans were acquired using two scanner systems (Medit i500 and iTero Element 2). Scanner type was not treated as an analytical variable, as the study focused on inter-rater reliability of tooth wear indices. Both systems provide high-resolution full-arch digital models suitable for qualitative tooth wear assessment; therefore, scans were pooled for analysis. All scans were exported in a high-resolution mesh format (.stl or .ply files) and analyzed with standard dental imaging software capable of rendering full-arch three-dimensional digital models. To ensure consistency, all scans were assessed under identical conditions, using the same type of display monitor and lighting. All intraoral scans were analyzed using Media Link software (version 3.3.6; Medit Corp., Seoul, South Korea), which was used for storage, retrieval, and visualization of STL files. Both examiners used the same software and the same software version throughout the study. Three-dimensional models were assessed using standard visualization functions provided by the software, including free rotation and zooming. No sectioning tools, slicing functions, or automated measurement features were applied. All evaluations were based on visual inspection of the digital models. To ensure consistency, both examiners worked under identical software environments, using the same software version, display monitor type, and ambient lighting conditions. Intraoral scans were included if they originated from adult patients (≥ 18 years), the digital model contained at least 14 teeth, and at least 50% of the tooth surface area was free from extensive restorations, prosthetic reconstructions, or large fillings. Scans presenting major artifacts, incomplete arches, or extensive restorative coverage preventing reliable assessment were excluded from the analysis. Two independent examiners, trained and familiarized with the scoring protocols, evaluated each scan using both indices (BEWE and the TWES 2.0 Tooth Wear Screening module only). Both examiners had more than five years of clinical experience in restorative and preventive dentistry. Prior to formal data collection, a calibration process was conducted using a representative set of intraoral scans that were not included in the final analysis. During this pilot calibration session, both examiners jointly reviewed the scoring criteria for BEWE and TWES 2.0 and discussed borderline cases. Calibration was repeated until a satisfactory level of agreement was achieved, after which all study scans were evaluated independently without further discussion. Each sextant was scored independently by both raters, and the results were recorded separately to allow assessment of interobserver reliability. Missing sextant-level data resulted from localized scan limitations, such as incomplete capture of specific sextants, artifacts, or extensive restorative coverage preventing reliable scoring. Only paired sextant observations evaluated by both examiners were included in inter-rater reliability analyses, which explains minor variations in sample size across sextants. Each participant was scanned once using a single intraoral scanner (either Medit i500 or iTero Element 2). Each intraoral scan was independently evaluated by two examiners using both screening indices (BEWE and the TWES 2.0 Tooth Wear Screening Module). For BEWE, one score per sextant was recorded, resulting in six BEWE scores per participant per examiner. For TWES 2.0, one score per sextant was recorded for all sextants, with an additional score recorded for the palatal surface in Sextant 2, in accordance with the screening protocol. Accordingly, for each participant, each examiner generated six BEWE scores and seven TWES 2.0 scores. The additional palatal surface score in Sextant 2 was analyzed as a distinct data point within the TWES 2.0 dataset and did not affect the BEWE scoring scheme. All scores were treated as separate data points for statistical analysis. As intraoral scan–derived mesh files do not contain color or material-specific information, enamel and dentin involvement was assessed based on morphological features of tooth wear, in accordance with the original clinical definitions of BEWE and TWES 2.0. All intraoral scans were evaluated independently by both examiners. The examiners were blinded to each other’s scores and did not have access to their previously recorded scores during the scoring phase. Scoring was performed without knowledge of patient identifiers or clinical information beyond the digital models. To minimize potential order effects, scans were assessed in a randomized order for each examiner. Once recorded, scores were not revised or modified. All evaluations were completed independently, without discussion between examiners during the scoring phase. 2.4 Sample size consideration This retrospective reliability study included 246 intraoral scans, generating multiple sextant-level observations assessed independently by two examiners. The resulting sample size is comparable to or exceeds that of previously published reliability studies evaluating tooth wear indices and digital assessment methods. Moreover, methodological literature indicates that reliability analyses based on more than 100 subjects and multiple observations per subject provide sufficient precision for intraclass correlation coefficient estimation. Accordingly, the sample size was considered adequate to detect meaningful levels of agreement and to minimize the risk of type II error. 2.5 Statistical analysis Continuous variables were summarized using descriptive statistics, including the number of observations, mean, median, standard deviation (SD), first and third quartiles (Q1, Q3), and range (minimum–maximum). Data distribution and skewness were visualized using boxplots, with the median indicated by the line within the box, the mean by a larger dot, and outliers represented as smaller points. Inter-rater agreement between repeated measurements was assessed using multiple complementary approaches: Wilcoxon signed-rank tests were applied to compare paired, non-normally distributed scores between Rater 1 and Rater 2. Intraclass correlation coefficients (ICCs) with 95% confidence intervals were calculated using a two-way random-effects model for absolute agreement [ICC(2,1)] to quantify inter-rater reliability across examiners. Bland–Altman plots were generated to visually examine agreement and identify potential systematic biases between raters, with limits of agreement defined as mean difference ± 1.96 × SD. All statistical analyses were conducted using R software (version 4.4). 3 Results 3.1 Study population characteristics The study included a total of 246 unique patients. Table 1 summarizes the distribution of sex within the study population: 129 (52.4%) were female, 66 (26.8%) were male, and 51 (20.7%) had unknown or unreported sex. Missing sex data resulted from the retrospective and anonymized nature of the dataset and did not affect the reliability analyses, as sex was not used as an analytical variable. The population provided a representative sample for evaluating intraoral scan-based tooth wear assessments. All intraoral scans originated from adult patients aged ≥ 18 years. Due to the retrospective and anonymized nature of the dataset, no additional patient-level information (e.g., age, medical history, or behavioral factors) was available beyond sex. Table 1 Baseline characteristics – sex Variable Parameter Overall (N = 246) Sex Female 52.4% (N = 129) Male 26.8% (N = 66) Unknown 20.7% (N = 51) Sex information was unavailable for 51 cases due to data anonymization. 3.2 BEWE For each sextant, analyses were based on paired observations from both raters; therefore, the reported sample size (N) represents the number of sextants evaluated by both examiners. Table 2 presents a comparison of BEWE scores between Rater 1 and Rater 2 across all sextants and the total score. Across sextants, Rater 1 consistently recorded slightly higher scores than Rater 2, with statistically significant differences observed in all sextants (p < 0.05 for each). Median values remained consistent between raters, and the largest discrepancies were noted in Sextant 3 and the total BEWE score. Table 2 Comparison of selected variables by Rater Variable Parameter Overall (paired observations) Rater 1 (N = 246) Rater 2 (N = 246) Difference test p-value Sextant 1 N 241 241 241 241 Wilcoxon 0.0039 (S) Mean (SD) 1.87 (0.67) 1.9 (0.69) 1.83 (0.65) -0.08 (0.42) Median (Q1-Q3) 2 (1–2) 2 (1–2) 2 (1–2) 0 (0–0) Range 0–3 0–3 0–3 -2–1 Sextant 2 N 246 246 246 246 Wilcoxon 0.0066 (S) Mean (SD) 2.41 (0.73) 2.45 (0.73) 2.38 (0.73) -0.07 (0.39) Median (Q1-Q3) 3 (2–3) 3 (2–3) 3 (2–3) 0 (0–0) Range 1–3 1–3 1–3 -1–1 Sextant 3 N 238 238 238 238 Wilcoxon < 0.001 (S) Mean (SD) 1.89 (0.64) 1.96 (0.64) 1.82 (0.63) -0.14 (0.42) Median (Q1-Q3) 2 (1–2) 2 (2–2) 2 (1–2) 0 (0–0) Range 1–3 1–3 1–3 -2–1 Sextant 4 N 245 245 245 245 Wilcoxon 0.0137 (S) Mean (SD) 2.04 (0.66) 2.07 (0.66) 2.01 (0.66) -0.06 (0.36) Median (Q1-Q3) 2 (2–2) 2 (2–3) 2 (2–2) 0 (0–0) Range 1–3 1–3 1–3 -1–1 Sextant 5 N 246 246 246 246 Wilcoxon < 0.001 (S) Mean (SD) 2.55 (0.65) 2.59 (0.62) 2.51 (0.67) -0.08 (0.37) Median (Q1-Q3) 3 (2–3) 3 (2–3) 3 (2–3) 0 (0–0) Range 0–3 1–3 0–3 -2–2 Sextant 6 N 238 238 238 238 Wilcoxon < 0.001 (S) Mean (SD) 2.02 (0.66) 2.07 (0.66) 1.97 (0.66) -0.1 (0.41) Median (Q1-Q3) 2 (2–2) 2 (2–3) 2 (2–2) 0 (0–0) Range 1–3 1–3 1–3 -2–1 Total BEWE N 246 246 246 246 Wilcoxon < 0.001 (S) Mean (SD) 12.64 (2.92) 12.88 (2.9) 12.4 (2.92) -0.48 (1.43) Median (Q1-Q3) 13 (11–14.25) 13 (11–15) 13 (11–14) 0 (-1–0) Range 5–18 5–18 5–18 -6–9 Inter-rater reliability was further assessed using intraclass correlation coefficients (ICCs) and Bland–Altman plots. ICC values ranged from 0.761 to 0.852 across sextants, indicating good agreement between examiners (Table 3 ). Bland–Altman analysis showed that most differences were small (–1 to + 1), with no evidence of systematic bias, supporting overall consistency of BEWE assessments. Agreement patterns between raters were generally consistent across sextants, with most discrepancies occurring within adjacent BEWE score categories. N represents the number of paired sextant-level observations evaluated by both raters and included in paired analyses (Wilcoxon signed-rank test and ICC). S indicates statistical significance (p < 0.05); NS indicates non-significance. Table 3 ICC - BEWE Variable ICC (95% CI) Statistic p-value Sextant 1 0.804 (0.752–0.845) 9.45 < 0.001 Sextant 2 0.852 (0.812–0.883) 12.78 < 0.001 Sextant 3 0.761 (0.678–0.821) 8.02 < 0.001 Sextant 4 0.851 (0.812–0.883) 12.69 < 0.001 Sextant 5 0.826 (0.778–0.864) 10.93 < 0.001 Sextant 6 0.803 (0.747–0.847) 9.61 < 0.001 3.3 TWES 2.0 (Screening module) For each sextant, analyses were based on paired observations from both raters; therefore, the reported sample size (N) represents the number of sextant-level observations evaluated by both examiners. For the TWES 2.0 Screening Module, the palatal surface in Sextant 2 was analyzed as an additional paired observation. Table 4 presents TWES 2.0 Screening scores across all sextants for Rater 1 and Rater 2. Small but statistically significant differences were observed in most sextants, with Rater 1 generally assigning slightly higher scores (differences ranged from 0.05 to 0.13 points). Table 4 Comparison of selected variables by Rater Variable Parameter Overall (paired observations) Rater 1 (N = 246) Rater 2 (N = 246) Difference test p-value Sextant 1 N 237 237 237 237 Wilcoxon 0.0243 (S) Mean (SD) 1.17 (0.39) 1.19 (0.42) 1.14 (0.36) -0.05 (0.37) Median (Q1-Q3) 1 (1–1) 1 (1–1) 1 (1–1) 0 (0–0) Range 0–3 0–3 0–2 -1–1 Sextant 2 inc. N 245 245 245 245 Wilcoxon < 0.001 (S) Mean (SD) 1.53 (0.59) 1.59 (0.61) 1.47 (0.56) -0.13 (0.52) Median (Q1-Q3) 2 (1–2) 2 (1–2) 1 (1–2) 0 (0–0) Range 0–4 0–4 0–3 -1–1 Sextant 2 pal. N 244 244 244 244 Wilcoxon < 0.001 (S) Mean (SD) 0.96 (0.83) 1.02 (0.84) 0.89 (0.81) -0.13 (0.56) Median (Q1-Q3) 1 (0–1) 1 (0–1) 1 (0–1) 0 (0–0) Range 0–3 0–3 0–3 -2–1 Sextant 3 N 234 234 234 234 Wilcoxon < 0.001 (S) Mean (SD) 1.14 (0.41) 1.18 (0.44) 1.1 (0.38) -0.08 (0.34) Median (Q1-Q3) 1 (1–1) 1 (1–1) 1 (1–1) 0 (0–0) Range 0–3 0–3 0–3 -1–1 Sextant 4 N 243 243 243 243 Wilcoxon 0.0128 (S) Mean (SD) 1.21 (0.41) 1.24 (0.43) 1.19 (0.4) -0.05 (0.33) Median (Q1-Q3) 1 (1–1) 1 (1–1) 1 (1–1) 0 (0–0) Range 0–2 1–2 0–2 -1–1 Sextant 5 N 246 246 246 246 Wilcoxon < 0.001 (S) Mean (SD) 1.72 (0.55) 1.78 (0.55) 1.65 (0.54) -0.13 (0.47) Median (Q1-Q3) 2 (1–2) 2 (1.25–2) 2 (1–2) 0 (0–0) Range 0–3 0–3 0–3 -1–2 Sextant 6 N 237 237 237 237 Wilcoxon 0.0014 (S) Mean (SD) 1.22 (0.47) 1.27 (0.46) 1.18 (0.47) -0.09 (0.42) Median (Q1-Q3) 1 (1–1) 1 (1–2) 1 (1–1) 0 (0–0) Range 0–3 0–3 0–2 -2–1 ICC analysis indicated moderate to good inter-rater reliability, with values ranging from 0.543 (Sextant 1) to 0.761 (Sextant 2 palatal) (Table 5 ). Bland–Altman plots demonstrated generally good agreement between raters, with the majority of differences within ± 1, and no consistent systematic bias across sextants. Agreement patterns between raters were consistent, with most discrepancies occurring within adjacent TWES 2.0 score categories, reflecting overall stability of the screening results. N represents the number of paired sextant-level observations evaluated by both raters and included in paired analyses (Wilcoxon signed-rank test and ICC). S indicates statistical significance (p < 0.05); NS indicates non-significance. Table 5 ICC - TWES Variable ICC (95% CI) Statistic p-value Sextant 1 0.543 (0.449–0.626) 3.42 < 0.001 Sextant 2 inc. 0.588 (0.493–0.668) 4.01 < 0.001 Sextant 2 pal. 0.761 (0.697–0.813) 7.71 < 0.001 Sextant 3 0.648 (0.564–0.719) 4.86 < 0.001 Sextant 4 0.677 (0.602–0.740) 5.29 < 0.001 Sextant 5 0.620 (0.524–0.698) 4.49 < 0.001 Sextant 6 0.590 (0.498–0.668) 4.00 < 0.001 4 Discussion The present study assessed inter-rater reliability and patterns of tooth wear using two indices—BEWE and TWES 2.0—based on 246 anonymized intraoral scans. The results provide important insights into the reproducibility and clinical utility of these tools for evaluating noncarious tooth wear. 4.1 Inter-rater reliability The good inter-rater reliability observed for BEWE in the present study is consistent with previous clinical investigations, which have demonstrated that BEWE provides reproducible and clinically robust assessments of erosive tooth wear when applied by calibrated examiners [ 12 , 19 – 21 ]. According to the interpretive guidelines proposed by Koo and Li, intraclass correlation coefficient (ICC) values below 0.50 indicate poor reliability, values between 0.50 and 0.75 indicate moderate reliability, values between 0.75 and 0.90 indicate good reliability, and values above 0.90 indicate excellent reliability [ 23 ]. Based on these criteria, BEWE demonstrated good inter-examiner reliability, whereas TWES 2.0 showed moderate to good reliability across sextants. For BEWE, intraclass correlation coefficients (ICCs) ranged from 0.761 to 0.852, indicating good reliability across all sextants. Bland–Altman analysis demonstrated that most differences between raters were small and balanced, with few outliers. These findings suggest that BEWE is a consistent tool for clinical screening of erosive tooth wear, demonstrating good inter-examiner agreement in the present study. The consistency of BEWE scores confirms its suitability for routine clinical practice and epidemiological studies. TWES 2.0 (Screening Module) demonstrated moderate to good inter-rater reliability, with ICC values ranging from 0.543 to 0.761. While reliability was slightly lower than for BEWE, Bland–Altman plots indicated generally good agreement, with most differences within ± 1. Minor variability observed in certain sextants—particularly posterior and palatal surfaces—likely reflects the increased difficulty of grading subtle wear patterns in these regions rather than a systematic bias. The slightly lower inter-rater reliability observed for TWES 2.0 compared with BEWE is in line with previous reports, which indicate that more detailed, surface-specific tooth wear indices tend to be associated with increased examiner dependence, particularly when subtle wear changes are evaluated [ 13 , 14 , 22 , 24 ]. Although statistically significant differences between raters were observed for BEWE scores across sextants, these differences were generally small and predominantly confined to adjacent score categories. From a clinical perspective, such minor discrepancies are unlikely to result in systematic changes in BEWE risk category classification, which is the primary basis for clinical decision-making and patient management. Therefore, the observed statistical significance should be interpreted with caution, as it does not necessarily indicate clinically meaningful disagreement between examiners. In the TWES 2.0 Screening Module, inter-rater reliability varied across sextants and surfaces. Higher agreement was observed for anterior and palatal surfaces, particularly in the palatal component of Sextant 2, which demonstrated the highest ICC value. This finding may be explained by the typically more uniform and clearly demarcated wear patterns on palatal surfaces of maxillary anterior teeth, which are commonly affected by erosive processes and therefore easier to identify consistently on digital models. In contrast, slightly lower ICC values were noted in posterior sextants, likely reflecting the increased morphological complexity of occlusal surfaces and the more subtle transitions between wear grades in these regions. Differences between anterior and posterior regions therefore appear to be related to surface anatomy and visual interpretability rather than systematic examiner bias. Overall, these findings support the robustness of TWES 2.0 for screening purposes, while highlighting that posterior and occlusal surfaces may require particular attention during examiner calibration. While previous studies have evaluated tooth wear indices using clinical examinations and dental casts, evidence regarding their application to intraoral scan–derived digital models remains limited. The present findings therefore extend existing knowledge by demonstrating that both BEWE and TWES 2.0 can be applied with acceptable reliability in a fully digital assessment environment [ 15 , 17 , 18 ]. 4.2 Patterns of tooth wear For BEWE, scores were higher overall, reflecting a broader scoring range and the tool’s sensitivity to more pronounced erosive wear. The distribution of scores confirms BEWE’s suitability for rapid screening, while TWES 2.0 provides detailed surface-specific assessment. The higher inter-rater reliability of BEWE is likely related to index design. BEWE relies on broader categorical thresholds and records only the most severely affected surface per sextant, which may reduce interpretative ambiguity and promote more consistent scoring. The higher inter-rater reliability observed for BEWE compared with TWES 2.0 is consistent with previous clinical validation studies, which have highlighted the simplicity of the BEWE scoring system as a key factor contributing to its reproducibility and examiner agreement [ 12 ]. Previous studies have emphasized that the simplified categorical structure of BEWE supports consistent scoring and facilitates reliable use in routine clinical settings, which may explain its higher inter-rater reliability compared with more detailed indices [ 19 , 20 ]. In contrast, TWES 2.0 provides a more detailed, surface-specific assessment, increasing diagnostic resolution but also examiner dependence, particularly when subtle wear changes are evaluated. Greater variability was observed in posterior sextants, especially on occlusal surfaces, which may reflect anatomical complexity and less clearly defined transitions between wear grades. Greater variability observed on posterior and palatal surfaces has also been reported in previous studies and is commonly attributed to anatomical complexity, limited visual cues, and less distinct transitions between wear grades in these regions [ 8 , 14 , 18 , 22 , 24 , 25 ]. Differences between raters were generally small and confined to adjacent score categories, suggesting inherent subjectivity of ordinal grading rather than systematic calibration bias. Additionally, the absence of color and texture information in intraoral scans may disproportionately affect more detailed indices such as TWES 2.0, whereas BEWE appears more robust in a purely digital environment. TWES 2.0 scores were generally concentrated at the lower end of the scale (score 1), consistent with mild to moderate tooth wear in this adult population. Small but statistically significant differences between raters were observed in most sextants, with Rater 1 generally assigning slightly higher scores. These observations reflect minor inter-rater variability in grading subtle changes, particularly on posterior and palatal surfaces, without evidence of systematic bias. 4.3 Clinical implications When directly compared, BEWE demonstrated consistently higher inter-examiner reliability than the TWES 2.0 Screening Module across all sextants, as reflected by higher ICC values and overall Bland–Altman agreement patterns. TWES 2.0 showed moderate to good reliability, with greater variability between examiners, particularly on posterior and palatal surfaces. These differences likely reflect the more detailed and surface-specific scoring structure of TWES 2.0, which may increase sensitivity to subtle wear features but also examiner dependence. The observed reliability of BEWE and TWES 2.0 underscores their utility as practical screening tools in both routine dental practice and large-scale epidemiological studies. BEWE’s simplicity and ease of use make it particularly suitable for rapid risk assessment of dental erosion, whereas TWES 2.0 allows a more detailed evaluation of wear severity and differentiation of etiological mechanisms (erosion, attrition, abrasion). The combination of these indices can guide treatment planning and preventive strategies, enabling early identification and management of patients at risk of progressive tooth wear. Importantly, although statistically significant inter-rater differences were observed, these discrepancies were generally small and predominantly limited to adjacent score categories. From a clinical perspective, such differences are unlikely to result in changes in BEWE cumulative risk category classification, which forms the basis for clinical decision-making, including preventive counseling, monitoring intervals, and referral for restorative intervention. Consequently, the observed level of inter-examiner variability is not expected to alter treatment thresholds or patient management strategies in routine practice. BEWE may therefore be reliably used as a primary screening tool to stratify patients according to erosion risk and determine appropriate recall intervals, while TWES 2.0 can complement this approach by providing more detailed, surface-specific information to support individualized treatment planning and longitudinal monitoring. 4.4 Limitations Several limitations should be considered when interpreting the present findings. This study was retrospective and relied on anonymized intraoral scans, which limited the availability of patient-level data such as age beyond adulthood (≥ 18 years), dietary habits, oral hygiene behaviors, and parafunctional activity. In addition, sex information was unavailable for approximately 20% of the sample due to data anonymization. Missing sextant-level data resulted from localized scan limitations, including incomplete capture, artifacts, or extensive restorative coverage preventing reliable scoring. Only paired sextant observations assessed by both examiners were included in the reliability analyses, leading to minor variations in sample size across sextants. The study focused exclusively on inter-rater reliability; intra-rater reliability was not assessed and should be addressed in future studies to further characterize examiner consistency over time. Furthermore, while intraclass correlation coefficients and Bland–Altman plots were used to assess agreement, detailed numerical Bland–Altman parameters (mean bias and limits of agreement) were not reported in detail and should be addressed in future studies. Finally, all assessments were performed using digital intraoral scan–derived models without color or texture information. Although identical software environments were used by both examiners, the absence of optical cues inherent to clinical examination may have influenced scoring, particularly for more detailed indices such as TWES 2.0. 4.5 Future research directions Future studies should aim to validate the application of BEWE and TWES 2.0 in prospective clinical settings, combining intraoral scan–based assessment with direct chairside examination. Longitudinal studies using repeated intraoral scans would be particularly valuable to evaluate the ability of these indices to monitor tooth wear progression over time. Further research may also compare the performance of different intraoral scanners and visualization software platforms to determine whether hardware- or software-related factors influence scoring reliability. Finally, the integration of automated or artificial intelligence–based wear detection tools may enhance objectivity and efficiency, potentially complementing established screening indices in digital workflows. 5 Conclusions BEWE and TWES 2.0 are reliable and practical tools for the assessment of noncarious tooth wear. Inter-rater reliability was generally good, with Bland–Altman analyses showing that most differences between examiners were within acceptable limits and without evidence of systematic bias. While BEWE demonstrated slightly higher consistency across sextants, TWES 2.0 allows more detailed evaluation of wear severity, particularly on occlusal and palatal surfaces. These findings support the use of both indices in clinical practice and research for standardized monitoring, early detection, and risk assessment of tooth wear. Examiner calibration remains critical to ensure reproducibility, especially when using TWES 2.0. Declarations Data availability The datasets generated and/or analyzed during the current study are not publicly available due to patient confidentiality and privacy restrictions, but anonymized summary data supporting the results are available from the corresponding author upon reasonable request. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work, the author(s) used ChatGPT (OpenAI) to assist in refining sentence structure and clarity. After using this tool, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the publication. Ethics approval and consent to participate - The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Bioethics Committee at the District Medical Chamber in Kraków, Poland (approval number: L.dz.OIL/KBL/22/2025, issued on 27 May 2025). Due to the retrospective and anonymized nature of the study, individual patient consent was not required; written permission to use the scans was provided by all three participating dental clinics. Consent for publication - Not applicable Availability of data and materials - All data generated and analyzed during this study are included in this published article. The original intraoral scans are not publicly available due to ethical and privacy restrictions but are available from the corresponding author upon reasonable request. Competing interests - The authors declare that they have no competing interests Funding - This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions – Maria Lorens contributed to the conception and design of the review, conducted the literature search, organized and curated the data, prepared tables, and drafted the manuscript. She also participated in critical revisions, approved the final version, and accepts responsibility for the integrity of the work. Dr. Iwona Tomaszewska contributed to the hypothesis development and design, supported the literature search and data interpretation, and critically revised the manuscript. She approved the final version and accepts responsibility for the integrity of the work. All authors critically reviewed the manuscript and made significant intellectual contributions. Finally, all authors approved the final version of the manuscript. Acknowledgements - The authors would like to thank Biostat Research and Development for providing statistical analysis support for this study. References Addy M, Shellis RP (2006) Interaction between attrition, abrasion and erosion in tooth wear. Monogr Oral Sci 20:17–31. https://doi.org/10.1159/000093348 Lopez-Frias FJ, Castellanos-Cosano L, Martin-Gonzalez J, Llamas-Carreras JM, Segura-Egea JJ (2012) Clinical measurement of tooth wear: tooth Wear Indices. J Clin Exp Dent 4:e48–e53. https://doi.org/10.4317/jced.50592 Bartlett D, O'Toole S (2019) Tooth wear and aging. Aust Dent J 64:S59–S62. https://doi.org/10.1111/adj.12681 Kanaan M, Brabant A, Eckert GJ, Hara AT, Carvalho JC (2022) Tooth wear and oral-health-related quality of life in dentate adults. J Dent 125:104269. https://doi.org/10.1016/j.jdent.2022.104269 FDI World Dental Federation (2024) Tooth wear. Int Dent J 74:163–164. https://doi.org/10.1016/j.identj.2023.10.007 Van’t Spijker A, Rodriguez JM, Kreulen CM, Bronkhorst EM, Bartlett DW, Creugers NHJ (2009) Prevalence of tooth wear in adults. Int J Prosthodont 22:35–42 Gillborg S, Åkerman S, Ekberg E (2019) Tooth wear in Swedish adults—a cross-sectional study. J Oral Rehabil 47:235–245. https://doi.org/10.1111/joor.12887 Bartlett DW, Lussi A, West NX, Bouchard P, Sanz M, Bourgeois D (2013) Prevalence of tooth wear on buccal and lingual surfaces and possible risk factors in young European adults. J Dent 41:1007–1013. https://doi.org/10.1016/j.jdent.2013.08.018 Bartlett D, O'Toole S (2020) Tooth wear: best evidence consensus statement. J Prosthodont 30:20–25. https://doi.org/10.1111/jopr.13312 Loomans B, Opdam N, Attin T, Bartlett D, Edelhoff D, Frankenberger R, Benic G, Ramseyer S, Wetselaar P, Sterenborg B, Hickel R, Pallesen U, Mehta S, Banerji S, Lussi A, Wilson N (2017) Severe tooth wear: European consensus statement on management guidelines. J Adhes Dent 19:111–119. https://doi.org/10.3290/j.jad.a38102 Smith BG, Knight JK (1984) An index for measuring the wear of teeth. Br Dent J 156:435–438. https://doi.org/10.1038/sj.bdj.4805394 Bartlett D, Ganss C, Lussi A (2008) Basic erosive wear examination (BEWE): a new scoring system for scientific and clinical needs. Clin Oral Investig 12:65–68. https://doi.org/10.1007/s00784-007-0181-5 Wetselaar P, Wetselaar-Glas MJM, Katzer LD, Ahlers MO (2020) Diagnosing tooth wear, a new taxonomy based on the revised version of the tooth wear evaluation system (TWES 2.0). J Oral Rehabil 47:703–712. https://doi.org/10.1111/joor.12972 Wetselaar P, Lobbezoo F (2016) The tooth wear evaluation system: a modular clinical guideline for the diagnosis and management planning of worn dentitions. J Oral Rehabil 43:69–80. https://doi.org/10.1111/joor.12340 Lorens M, Tomaszewska I (2025) Methods for assessing and measuring tooth wear—applications in clinical research and a comparison of the Basic Erosive Wear Examination, Tooth Wear Index and Tooth Wear Evaluation System Version 2.0. J Oral Rehabil. 10.1111/joor.70104 PMID:41247000 Schlenz MA, Schlenz MB, Wöstmann B, Jungert A, Ganss C (2022) Intraoral scanner-based monitoring of tooth wear in young adults: 12-month results. Clin Oral Investig 26(2):1869–1878. 10.1007/s00784-021-04162-6 da Rosa Moreira RT, Bastos PT, da Silva D, Normando (2021) Reliability of qualitative occlusal tooth wear evaluation using an intraoral scanner: a pilot study. PLoS ONE 16:e0249119. https://doi.org/10.1371/journal.pone.0249119 Mehta SB, Bronkhorst EM, Crins L, Huysmans M-CDNJ, Wetselaar P, Loomans BAC (2021) A comparative evaluation between the reliability of gypsum casts and digital greyscale intra-oral scans for the scoring of tooth wear using the Tooth Wear Evaluation System (TWES). J Oral Rehabil 48(6):678–686. 10.1111/joor.13141 Bartlett D, Dattani S, Mills I, Pitts N, Rattan R, Rochford D, Wilson NHF, Mehta S, O’Toole S (2019) Monitoring erosive toothwear: BEWE, a simple tool to protect patients and the profession. Br Dent J 226:930–932. https://doi.org/10.1038/s41415-019-0411-7 Bartlett D (2012) Summary of: evaluation of the basic erosive wear examination (BEWE) for use in general dental practice. Br Dent J 213:128–129. https://doi.org/10.1038/sj.bdj.2012.698 Aránguiz V, Lara JS, Marró ML, O’Toole S, Ramírez V, Bartlett D (2020) Recommendations and guidelines for dentists using the basic erosive wear examination index (BEWE). Br Dent J 228:153–157. https://doi.org/10.1038/s41415-020-1246-y Roehl JC, Katzer L, Jakstat HA, Wetselaar P, Ahlers MO (2025) Reliability of the assessment of tooth wear severity on dental hard tissues and dental restorations, using the TWES 2.0, by nonexperts. J Oral Rehabil 52:125–136. https://doi.org/10.1111/joor.13856 Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15(2):155–163. 10.1016/j.jcm.2016.02.012 Roehl JC, Jakstat HA, Becker K, Wetselaar P, Ahlers MO (2022) Tooth Wear Evaluation System (TWES) 2.0—Reliability of diagnosis with and without computer-assisted evaluation. J Oral Rehabil 49(1):81–91. 10.1111/joor.13277 Ganss C, Lussi A (2006) Diagnosis of erosive tooth wear. Monogr Oral Sci 20:32–43. https://doi.org/10.1159/000093349 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8502313","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":570572343,"identity":"766b6a75-5897-4f68-951e-832e56110b9c","order_by":0,"name":"Maria Lorens","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYJCCAwwMCQx8ELYNEDM2HiBKCxuEnQbS0kBQCwOSlsMwQ3AD3fazDw/8ABrOxt778HHBn/N2a9sPA22psYnGpcXsTLrBwR6GHAY2nuPGxjPbbidvO5MI1HIsLbcBl5YDaQwHeBgqGNgk0tikeRtuJ5sdAGphbDiMW8v5ZwwH/0C0sP/m+XMu2ez8QwJabqQxHOYBOQxoCzMP2wE7sxuEbLnxjOGwjEEaDxvPMWZp3rbkBLMbQFsS8PnlfBrzxzcVyXL87G2Mn3n+2NmbnU9/+OBDjQ1OLRBgwMADYyaCVSbgVY4G7ElRPApGwSgYBSMDAAAg9Vuo5F4IDgAAAABJRU5ErkJggg==","orcid":"","institution":"Jagiellonian University","correspondingAuthor":true,"prefix":"","firstName":"Maria","middleName":"","lastName":"Lorens","suffix":""},{"id":570572346,"identity":"794c1d77-930f-4265-b29c-3b8dd835406c","order_by":1,"name":"Iwona Tomaszewska","email":"","orcid":"","institution":"Jagiellonian University","correspondingAuthor":false,"prefix":"","firstName":"Iwona","middleName":"","lastName":"Tomaszewska","suffix":""}],"badges":[],"createdAt":"2026-01-02 16:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8502313/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8502313/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99818952,"identity":"bd4bafe5-5cfd-4b74-bf8d-1379ccc76664","added_by":"auto","created_at":"2026-01-08 14:56:21","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62115,"visible":true,"origin":"","legend":"","description":"","filename":"AssessmentofBEWEandTWESUtilizingScan.docx","url":"https://assets-eu.researchsquare.com/files/rs-8502313/v1/ee4df890add7dcd03dafb222.docx"},{"id":99818949,"identity":"d1968291-e2b8-4199-b2a6-7f51f25af12e","added_by":"auto","created_at":"2026-01-08 14:56:21","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5951,"visible":true,"origin":"","legend":"","description":"","filename":"9baa3ed0287c49489bd72df780c35e9c.json","url":"https://assets-eu.researchsquare.com/files/rs-8502313/v1/eafb75cc3235369d7b29d177.json"},{"id":99818944,"identity":"8e12a0be-a3ef-4a57-8bd2-7a7cf1336ac7","added_by":"auto","created_at":"2026-01-08 14:56:20","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":130717,"visible":true,"origin":"","legend":"","description":"","filename":"9baa3ed0287c49489bd72df780c35e9c1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8502313/v1/ccc19a04464fadaf13794e84.xml"},{"id":99818941,"identity":"73438404-2e00-4497-afc6-528ede7801f2","added_by":"auto","created_at":"2026-01-08 14:56:19","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125465,"visible":true,"origin":"","legend":"","description":"","filename":"9baa3ed0287c49489bd72df780c35e9c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8502313/v1/a64f098c443a754ff207f1b3.xml"},{"id":99818946,"identity":"b7112552-3339-4c46-b9f4-a765168c8962","added_by":"auto","created_at":"2026-01-08 14:56:21","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":138919,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8502313/v1/28b0cd3cd8117761b29e867a.html"},{"id":100563361,"identity":"115f489c-c53f-43f1-a538-7357904f2d7b","added_by":"auto","created_at":"2026-01-19 08:46:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1314331,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8502313/v1/c3eab8d9-acb0-4613-ba60-673d58a9a5b1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Reliability Assessment of the Basic Erosive Wear Examination and the Tooth Wear Evaluation System 2.0 Utilizing Intraoral Scan Data","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTooth wear is a multifactorial condition characterized by the non-carious loss of dental hard tissues. It results from three principal mechanisms\u0026mdash;erosion, attrition, and abrasion\u0026mdash;which may act independently or synergistically [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], with erosion often considered the predominant factor [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As an irreversible process, tooth wear progressively compromises enamel and dentin, leading to clinical problems such as dentin hypersensitivity, loss of vertical dimension, impaired esthetics [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is also increasingly recognized as a clinically relevant condition requiring early identification and monitoring [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prevalence of tooth wear has increased worldwide and is now frequently observed across all age groups, with a particularly concerning rise among younger patients [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Epidemiological studies report a wide prevalence range of tooth wear in adult populations, varying from approximately 20% to over 50%, depending on age, diagnostic criteria, and assessment method. While early-stage tooth wear is often asymptomatic, moderate to severe forms have been associated with functional impairments, including loss of occlusal vertical dimension, compromised masticatory efficiency, dentin hypersensitivity, and increased need for complex restorative interventions. Importantly, several studies indicate that a substantial proportion of younger adults already exhibit early signs of erosive and attritional wear, underscoring the clinical relevance of early detection and preventive screening strategies. In this context, the use of standardized, reproducible screening indices is essential to enable timely diagnosis, risk stratification, and monitoring of disease progression [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Lifestyle- and diet-related factors, parafunctional habits, and demographic influences contribute to its multifactorial etiology. This growing burden highlights the need for reliable diagnostic and monitoring methods that can be applied effectively in everyday clinical practice [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo promote standardization in the assessment of tooth wear, the 2017 European Consensus Statement recommended three principal indices: the Basic Erosive Wear Examination (BEWE), the Tooth Wear Index (TWI), and the Tooth Wear Evaluation System 2.0 (TWES 2.0) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These tools were identified as valid frameworks for recording the severity and distribution of wear. However, their clinical usefulness differs. Although the Tooth Wear Index (TWI) remains a valuable reference for detailed epidemiological and research purposes, its extensive scoring system and time-consuming application limit its feasibility for rapid screening and routine clinical use [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. For this reason, the present study focused exclusively on BEWE and the Tooth Wear Screening Module of TWES 2.0, which are designed for efficient clinical screening [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. By contrast, the BEWE and TWES 2.0 include simplified scoring systems designed to facilitate rapid chairside assessment, making them more practical as screening tools [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In particular, the Tooth Wear Screening component of TWES 2.0 is especially well-suited for initial examinations, providing a quick overview of tooth wear status that can efficiently guide further diagnostic or preventive steps.\u003c/p\u003e \u003cp\u003eWhile both BEWE and TWES 2.0 are recommended for clinical assessment of tooth wear, they differ in scope and level of detail. BEWE is a simplified screening index primarily focused on erosive tooth wear, offering high feasibility and ease of use in routine clinical practice, but providing limited information on wear distribution and etiology. In contrast, TWES 2.0 adopts a broader conceptual framework that encompasses erosion, attrition, and abrasion, allowing for a more detailed surface-specific assessment, particularly on occlusal and palatal surfaces. This increased diagnostic detail, however, may come at the cost of greater examiner dependence and potentially reduced reproducibility. Comparing these indices when applied to intraoral scans may therefore provide valuable insights into their relative reliability and practical applicability in digital workflows.\u003c/p\u003e \u003cp\u003eAlthough several indices are available for assessing tooth wear, BEWE and the Tooth Wear Screening Module of TWES 2.0 were selected for this study due to their feasibility for routine clinical use and compatibility with digital workflows. Both indices are designed for rapid screening and rely on simplified, ordinal scoring systems that can be readily applied to intraoral scans. In contrast, the Tooth Wear Index (TWI), while valuable for detailed epidemiological assessment, requires extensive surface-level scoring and is less suited to time-efficient screening or digital assessment. Previous studies have also highlighted the practical advantages of BEWE and TWES 2.0 when applied in a digital context, supporting their use as screening tools in contemporary dental practice [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith the increasing integration of intraoral scanners in dentistry, high-resolution three-dimensional digital models of dentitions offer novel opportunities for tooth wear assessment using digital models [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This development raises the question of whether screening indices such as BEWE and TWES 2.0 can be reliably applied in a digital environment and, importantly, how clinically useful they are for early detection, monitoring, and treatment planning.\u003c/p\u003e \u003cp\u003ePrevious studies have evaluated tooth wear indices using clinical examinations, dental casts, and, in selected reports, digital models [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, available evidence remains heterogeneous, and data on the inter-rater reliability of BEWE and TWES 2.0 when applied exclusively to intraoral scan\u0026ndash;derived STL files are still limited. In particular, it remains unclear whether these screening indices maintain consistent examiner agreement when assessments rely solely on three-dimensional digital models rather than direct clinical inspection [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Given the increasing use of intraoral scanners in routine dental workflows, establishing the reproducibility of BEWE and TWES 2.0 in a fully digital context is clinically meaningful.\u003c/p\u003e \u003cp\u003eThe objective of this study was to evaluate and compare the inter-rater reliability of the Basic Erosive Wear Examination (BEWE) and the Tooth Wear Screening Module of the Tooth Wear Evaluation System 2.0 (TWES 2.0) when applied to intraoral scan\u0026ndash;derived digital models. Specifically, the study aimed to assess examiner agreement across sextants using standardized reliability metrics.\u003c/p\u003e \u003cp\u003eThe null hypothesis was that no significant differences would exist between the scores assigned by the two examiners for either BEWE or TWES 2.0 when applied to intraoral scans, and that both indices would demonstrate comparable inter-rater reliability.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and ethical considerations\u003c/h2\u003e \u003cp\u003eThis retrospective study analyzed anonymized intraoral scans to evaluate the reliability and clinical applicability of two tooth wear screening indices: the Basic Erosive Wear Examination (BEWE) and the Tooth Wear Screening module of the Tooth Wear Evaluation System 2.0 (TWES 2.0). The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Bioethics Committee at the District Medical Chamber in Krak\u0026oacute;w, Poland (approval number: L.dz.OIL/KBL/22/2025, issued on 27 May 2025). Due to the retrospective and anonymized nature of the data, individual patient consent was not required; written permission to use the scans was obtained from all three participating dental clinics.\u003c/p\u003e \u003cp\u003eThis study evaluated whether the BEWE and the TWES 2.0 Screening Module can be applied consistently to intraoral scan\u0026ndash;derived digital models. Because no chairside clinical examinations were available, the analysis focused on inter-rater reliability rather than validation against a clinical reference standard.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Description of indices\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 BEWE\u003c/h2\u003e \u003cp\u003eThe Basic Erosive Wear Examination (BEWE), is a standardized and widely applied screening tool for erosive tooth wear [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The dentition is divided into sextants, and in each sextant the surface with the most severe lesion is recorded. Third molars are excluded. The sextants were defined as follows: Sextant 1 (maxillary right posterior: teeth 14\u0026ndash;17), Sextant 2 (maxillary anterior: teeth 13\u0026ndash;23), Sextant 3 (maxillary left posterior: teeth 24\u0026ndash;27), Sextant 4 (mandibular left posterior: teeth 34\u0026ndash;37), Sextant 5 (mandibular anterior: teeth 33\u0026ndash;43), and Sextant 6 (mandibular right posterior: teeth 44\u0026ndash;47). Surfaces are scored on a four-point ordinal scale according to the following criteria:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eScore 0 \u0026ndash; No evidence of erosive tooth wear.\u003c/p\u003e\u003cp\u003eScore 1 \u0026ndash; Initial loss of surface texture, with early enamel softening but without distinct tissue loss.\u003c/p\u003e\u003cp\u003eScore 2 \u0026ndash; Distinct hard tissue loss involving less than 50% of the surface area.\u003c/p\u003e\u003cp\u003eScore 3 \u0026ndash; Hard tissue loss involving more than 50% of the surface area.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe cumulative BEWE score is obtained by summing the sextant scores across the full dentition. This sum can then be used to stratify patients into risk categories (none, low, medium, or high risk), which are linked to clinical recommendations for monitoring and management. The system is designed to be simple, reproducible, and feasible for rapid chairside application, while still allowing clinicians to make informed decisions about preventive or restorative needs.\u003c/p\u003e \u003cp\u003eFor the purposes of the present study, however, BEWE scores were recorded and analyzed separately for each sextant, with each sextant constituting an individual unit of analysis; the cumulative BEWE score was not used for statistical comparisons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Tooth wear evaluation system 2.0 (TWES 2.0)\u003c/h2\u003e \u003cp\u003eThe Tooth Wear Evaluation System 2.0 (TWES 2.0) is a standardized framework for assessing non-carious tooth wear caused by erosion, attrition, and abrasion [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. TWES 2.0 consists of two modules: the Tooth Wear Screening Module, intended for rapid clinical or epidemiological assessments, and the Tooth Wear Status Module, which allows a more detailed evaluation for treatment planning and comprehensive documentation. While the Status module enables extended analysis of individual surfaces and severity, in this study we focused exclusively on the Screening Module, which facilitates quick, reliable assessment in routine practice and large-scale studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the Tooth Wear Screening Module, the dentition is divided into sextants, and the most severely affected surface within each sextant is recorded. Palatal surfaces in Sextant 2 receive additional evaluation to ensure thorough assessment of this often-affected area. Scoring uses two separate five-point ordinal scales:\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e1. Occlusal and incisal surfaces:\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGrade 0: No visible wear\u003c/p\u003e \u003cp\u003eGrade 1: Wear limited to enamel\u003c/p\u003e \u003cp\u003eGrade 2: Exposed dentin with \u0026le;\u0026thinsp;1/3 loss of clinical crown height\u003c/p\u003e \u003cp\u003eGrade 3: Loss of crown height\u0026thinsp;\u0026gt;\u0026thinsp;1/3 but \u0026lt;\u0026thinsp;2/3\u003c/p\u003e \u003cp\u003eGrade 4: Loss of crown height\u0026thinsp;\u0026ge;\u0026thinsp;2/3\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e2. Non-occlusal surfaces (buccal and lingual):\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGrade 0: No visible wear\u003c/p\u003e \u003cp\u003eGrade 1: Wear limited to enamel\u003c/p\u003e \u003cp\u003eGrade 2: Exposed dentin affecting\u0026thinsp;\u0026lt;\u0026thinsp;50% of the surface\u003c/p\u003e \u003cp\u003eGrade 3: Exposed dentin affecting\u0026thinsp;\u0026ge;\u0026thinsp;50% of the surface\u003c/p\u003e \u003cp\u003eGrade 4: Complete enamel loss or pulp exposure\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eScores are recorded independently for each sextant, without summing across sextants. Clinical recommendations are guided by severity: Grades 0\u0026ndash;1 indicate routine monitoring, Grade 2 may prompt further evaluation, and Grades 3\u0026ndash;4 require a comprehensive Status module assessment. This modular approach ensures that the Screening Module remains efficient and practical, while the Status module can be applied when more detailed clinical assessment is needed.\u003c/p\u003e \u003cp\u003eAccordingly, in this study, each sextant score constituted a separate data point for statistical analysis.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection and examiner calibration\u003c/h2\u003e \u003cp\u003eA total of 246 intraoral scans from unique adult patients (\u0026ge;\u0026thinsp;18 years) were included in the study. Scans were acquired in two clinics using the Medit i500 scanner (Medit Corp., Seoul, South Korea) and in one clinic using the iTero Element 2 scanner (Align Technology, San Jose, CA, USA). Intraoral scans were acquired using two scanner systems (Medit i500 and iTero Element 2). Scanner type was not treated as an analytical variable, as the study focused on inter-rater reliability of tooth wear indices. Both systems provide high-resolution full-arch digital models suitable for qualitative tooth wear assessment; therefore, scans were pooled for analysis.\u003c/p\u003e \u003cp\u003eAll scans were exported in a high-resolution mesh format (.stl or .ply files) and analyzed with standard dental imaging software capable of rendering full-arch three-dimensional digital models. To ensure consistency, all scans were assessed under identical conditions, using the same type of display monitor and lighting.\u003c/p\u003e \u003cp\u003eAll intraoral scans were analyzed using Media Link software (version 3.3.6; Medit Corp., Seoul, South Korea), which was used for storage, retrieval, and visualization of STL files. Both examiners used the same software and the same software version throughout the study.\u003c/p\u003e \u003cp\u003eThree-dimensional models were assessed using standard visualization functions provided by the software, including free rotation and zooming. No sectioning tools, slicing functions, or automated measurement features were applied. All evaluations were based on visual inspection of the digital models.\u003c/p\u003e \u003cp\u003eTo ensure consistency, both examiners worked under identical software environments, using the same software version, display monitor type, and ambient lighting conditions.\u003c/p\u003e \u003cp\u003eIntraoral scans were included if they originated from adult patients (\u0026ge;\u0026thinsp;18 years), the digital model contained at least 14 teeth, and at least 50% of the tooth surface area was free from extensive restorations, prosthetic reconstructions, or large fillings. Scans presenting major artifacts, incomplete arches, or extensive restorative coverage preventing reliable assessment were excluded from the analysis.\u003c/p\u003e \u003cp\u003eTwo independent examiners, trained and familiarized with the scoring protocols, evaluated each scan using both indices (BEWE and the TWES 2.0 Tooth Wear Screening module only). Both examiners had more than five years of clinical experience in restorative and preventive dentistry. Prior to formal data collection, a calibration process was conducted using a representative set of intraoral scans that were not included in the final analysis. During this pilot calibration session, both examiners jointly reviewed the scoring criteria for BEWE and TWES 2.0 and discussed borderline cases. Calibration was repeated until a satisfactory level of agreement was achieved, after which all study scans were evaluated independently without further discussion. Each sextant was scored independently by both raters, and the results were recorded separately to allow assessment of interobserver reliability.\u003c/p\u003e \u003cp\u003eMissing sextant-level data resulted from localized scan limitations, such as incomplete capture of specific sextants, artifacts, or extensive restorative coverage preventing reliable scoring. Only paired sextant observations evaluated by both examiners were included in inter-rater reliability analyses, which explains minor variations in sample size across sextants.\u003c/p\u003e \u003cp\u003eEach participant was scanned once using a single intraoral scanner (either Medit i500 or iTero Element 2). Each intraoral scan was independently evaluated by two examiners using both screening indices (BEWE and the TWES 2.0 Tooth Wear Screening Module). For BEWE, one score per sextant was recorded, resulting in six BEWE scores per participant per examiner. For TWES 2.0, one score per sextant was recorded for all sextants, with an additional score recorded for the palatal surface in Sextant 2, in accordance with the screening protocol. Accordingly, for each participant, each examiner generated six BEWE scores and seven TWES 2.0 scores. The additional palatal surface score in Sextant 2 was analyzed as a distinct data point within the TWES 2.0 dataset and did not affect the BEWE scoring scheme. All scores were treated as separate data points for statistical analysis.\u003c/p\u003e \u003cp\u003eAs intraoral scan\u0026ndash;derived mesh files do not contain color or material-specific information, enamel and dentin involvement was assessed based on morphological features of tooth wear, in accordance with the original clinical definitions of BEWE and TWES 2.0.\u003c/p\u003e \u003cp\u003eAll intraoral scans were evaluated independently by both examiners. The examiners were blinded to each other\u0026rsquo;s scores and did not have access to their previously recorded scores during the scoring phase. Scoring was performed without knowledge of patient identifiers or clinical information beyond the digital models. To minimize potential order effects, scans were assessed in a randomized order for each examiner. Once recorded, scores were not revised or modified. All evaluations were completed independently, without discussion between examiners during the scoring phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sample size consideration\u003c/h2\u003e \u003cp\u003eThis retrospective reliability study included 246 intraoral scans, generating multiple sextant-level observations assessed independently by two examiners. The resulting sample size is comparable to or exceeds that of previously published reliability studies evaluating tooth wear indices and digital assessment methods. Moreover, methodological literature indicates that reliability analyses based on more than 100 subjects and multiple observations per subject provide sufficient precision for intraclass correlation coefficient estimation. Accordingly, the sample size was considered adequate to detect meaningful levels of agreement and to minimize the risk of type II error.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were summarized using descriptive statistics, including the number of observations, mean, median, standard deviation (SD), first and third quartiles (Q1, Q3), and range (minimum\u0026ndash;maximum). Data distribution and skewness were visualized using boxplots, with the median indicated by the line within the box, the mean by a larger dot, and outliers represented as smaller points.\u003c/p\u003e \u003cp\u003eInter-rater agreement between repeated measurements was assessed using multiple complementary approaches:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWilcoxon signed-rank tests were applied to compare paired, non-normally distributed scores between Rater 1 and Rater 2.\u003c/p\u003e\u003cp\u003eIntraclass correlation coefficients (ICCs) with 95% confidence intervals were calculated using a two-way random-effects model for absolute agreement [ICC(2,1)] to quantify inter-rater reliability across examiners.\u003c/p\u003e\u003cp\u003eBland\u0026ndash;Altman plots were generated to visually examine agreement and identify potential systematic biases between raters, with limits of agreement defined as mean difference\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 \u0026times; SD.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using R software (version 4.4).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study population characteristics\u003c/h2\u003e \u003cp\u003eThe study included a total of 246 unique patients. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the distribution of sex within the study population: 129 (52.4%) were female, 66 (26.8%) were male, and 51 (20.7%) had unknown or unreported sex. Missing sex data resulted from the retrospective and anonymized nature of the dataset and did not affect the reliability analyses, as sex was not used as an analytical variable. The population provided a representative sample for evaluating intraoral scan-based tooth wear assessments.\u003c/p\u003e \u003cp\u003eAll intraoral scans originated from adult patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years. Due to the retrospective and anonymized nature of the dataset, no additional patient-level information (e.g., age, medical history, or behavioral factors) was available beyond sex.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics \u0026ndash; sex\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;246)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.4% (N\u0026thinsp;=\u0026thinsp;129)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.8% (N\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7% (N\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSex information was unavailable for 51 cases due to data anonymization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 BEWE\u003c/h2\u003e \u003cp\u003eFor each sextant, analyses were based on paired observations from both raters; therefore, the reported sample size (N) represents the number of sextants evaluated by both examiners.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a comparison of BEWE scores between Rater 1 and Rater 2 across all sextants and the total score. Across sextants, Rater 1 consistently recorded slightly higher scores than Rater 2, with statistically significant differences observed in all sextants (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for each). Median values remained consistent between raters, and the largest discrepancies were noted in Sextant 3 and the total BEWE score.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of selected variables by Rater\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall (paired observations)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRater 1 (N\u0026thinsp;=\u0026thinsp;246)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRater 2 (N\u0026thinsp;=\u0026thinsp;246)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003etest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.0039 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9 (0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.83 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.08 (0.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.0066 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.41 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.45 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.38 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.07 (0.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89 (0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96 (0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82 (0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.14 (0.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.0137 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.07 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.01 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.06 (0.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.55 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59 (0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.51 (0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.08 (0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.07 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.97 (0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1 (0.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTotal BEWE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.64 (2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.88 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.4 (2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.48 (1.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (11\u0026ndash;14.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (11\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (11\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (-1\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInter-rater reliability was further assessed using intraclass correlation coefficients (ICCs) and Bland\u0026ndash;Altman plots. ICC values ranged from 0.761 to 0.852 across sextants, indicating good agreement between examiners (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Bland\u0026ndash;Altman analysis showed that most differences were small (\u0026ndash;1 to +\u0026thinsp;1), with no evidence of systematic bias, supporting overall consistency of BEWE assessments.\u003c/p\u003e \u003cp\u003eAgreement patterns between raters were generally consistent across sextants, with most discrepancies occurring within adjacent BEWE score categories.\u003c/p\u003e \u003cp\u003eN represents the number of paired sextant-level observations evaluated by both raters and included in paired analyses (Wilcoxon signed-rank test and ICC).\u003c/p\u003e \u003cp\u003eS indicates statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); NS indicates non-significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eICC - BEWE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.804 (0.752\u0026ndash;0.845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.852 (0.812\u0026ndash;0.883)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.761 (0.678\u0026ndash;0.821)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.851 (0.812\u0026ndash;0.883)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.826 (0.778\u0026ndash;0.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.803 (0.747\u0026ndash;0.847)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 TWES 2.0 (Screening module)\u003c/h2\u003e \u003cp\u003eFor each sextant, analyses were based on paired observations from both raters; therefore, the reported sample size (N) represents the number of sextant-level observations evaluated by both examiners. For the TWES 2.0 Screening Module, the palatal surface in Sextant 2 was analyzed as an additional paired observation.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents TWES 2.0 Screening scores across all sextants for Rater 1 and Rater 2. Small but statistically significant differences were observed in most sextants, with Rater 1 generally assigning slightly higher scores (differences ranged from 0.05 to 0.13 points).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of selected variables by Rater\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall (paired observations)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRater 1 (N\u0026thinsp;=\u0026thinsp;246)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRater 2 (N\u0026thinsp;=\u0026thinsp;246)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003etest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.0243 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.14 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05 (0.37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 2 inc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53 (0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59 (0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47 (0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.13 (0.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 2 pal.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89 (0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.13 (0.56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18 (0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1 (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.08 (0.34)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.0128 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21 (0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24 (0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05 (0.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.72 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78 (0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65 (0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.13 (0.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.25\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSextant 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWilcoxon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.0014 (S)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27 (0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.09 (0.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eICC analysis indicated moderate to good inter-rater reliability, with values ranging from 0.543 (Sextant 1) to 0.761 (Sextant 2 palatal) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Bland\u0026ndash;Altman plots demonstrated generally good agreement between raters, with the majority of differences within \u0026plusmn;\u0026thinsp;1, and no consistent systematic bias across sextants.\u003c/p\u003e \u003cp\u003e Agreement patterns between raters were consistent, with most discrepancies occurring within adjacent TWES 2.0 score categories, reflecting overall stability of the screening results.\u003c/p\u003e \u003cp\u003eN represents the number of paired sextant-level observations evaluated by both raters and included in paired analyses (Wilcoxon signed-rank test and ICC).\u003c/p\u003e \u003cp\u003eS indicates statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); NS indicates non-significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eICC - TWES\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.543 (0.449\u0026ndash;0.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 2 inc.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.588 (0.493\u0026ndash;0.668)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 2 pal.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.761 (0.697\u0026ndash;0.813)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.648 (0.564\u0026ndash;0.719)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.677 (0.602\u0026ndash;0.740)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.620 (0.524\u0026ndash;0.698)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSextant 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.590 (0.498\u0026ndash;0.668)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe present study assessed inter-rater reliability and patterns of tooth wear using two indices\u0026mdash;BEWE and TWES 2.0\u0026mdash;based on 246 anonymized intraoral scans. The results provide important insights into the reproducibility and clinical utility of these tools for evaluating noncarious tooth wear.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Inter-rater reliability\u003c/h2\u003e \u003cp\u003eThe good inter-rater reliability observed for BEWE in the present study is consistent with previous clinical investigations, which have demonstrated that BEWE provides reproducible and clinically robust assessments of erosive tooth wear when applied by calibrated examiners [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to the interpretive guidelines proposed by Koo and Li, intraclass correlation coefficient (ICC) values below 0.50 indicate poor reliability, values between 0.50 and 0.75 indicate moderate reliability, values between 0.75 and 0.90 indicate good reliability, and values above 0.90 indicate excellent reliability [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Based on these criteria, BEWE demonstrated good inter-examiner reliability, whereas TWES 2.0 showed moderate to good reliability across sextants.\u003c/p\u003e \u003cp\u003eFor BEWE, intraclass correlation coefficients (ICCs) ranged from 0.761 to 0.852, indicating good reliability across all sextants. Bland\u0026ndash;Altman analysis demonstrated that most differences between raters were small and balanced, with few outliers. These findings suggest that BEWE is a consistent tool for clinical screening of erosive tooth wear, demonstrating good inter-examiner agreement in the present study. The consistency of BEWE scores confirms its suitability for routine clinical practice and epidemiological studies.\u003c/p\u003e \u003cp\u003eTWES 2.0 (Screening Module) demonstrated moderate to good inter-rater reliability, with ICC values ranging from 0.543 to 0.761. While reliability was slightly lower than for BEWE, Bland\u0026ndash;Altman plots indicated generally good agreement, with most differences within \u0026plusmn;\u0026thinsp;1. Minor variability observed in certain sextants\u0026mdash;particularly posterior and palatal surfaces\u0026mdash;likely reflects the increased difficulty of grading subtle wear patterns in these regions rather than a systematic bias. The slightly lower inter-rater reliability observed for TWES 2.0 compared with BEWE is in line with previous reports, which indicate that more detailed, surface-specific tooth wear indices tend to be associated with increased examiner dependence, particularly when subtle wear changes are evaluated [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough statistically significant differences between raters were observed for BEWE scores across sextants, these differences were generally small and predominantly confined to adjacent score categories. From a clinical perspective, such minor discrepancies are unlikely to result in systematic changes in BEWE risk category classification, which is the primary basis for clinical decision-making and patient management. Therefore, the observed statistical significance should be interpreted with caution, as it does not necessarily indicate clinically meaningful disagreement between examiners.\u003c/p\u003e \u003cp\u003eIn the TWES 2.0 Screening Module, inter-rater reliability varied across sextants and surfaces. Higher agreement was observed for anterior and palatal surfaces, particularly in the palatal component of Sextant 2, which demonstrated the highest ICC value. This finding may be explained by the typically more uniform and clearly demarcated wear patterns on palatal surfaces of maxillary anterior teeth, which are commonly affected by erosive processes and therefore easier to identify consistently on digital models.\u003c/p\u003e \u003cp\u003eIn contrast, slightly lower ICC values were noted in posterior sextants, likely reflecting the increased morphological complexity of occlusal surfaces and the more subtle transitions between wear grades in these regions. Differences between anterior and posterior regions therefore appear to be related to surface anatomy and visual interpretability rather than systematic examiner bias. Overall, these findings support the robustness of TWES 2.0 for screening purposes, while highlighting that posterior and occlusal surfaces may require particular attention during examiner calibration.\u003c/p\u003e \u003cp\u003eWhile previous studies have evaluated tooth wear indices using clinical examinations and dental casts, evidence regarding their application to intraoral scan\u0026ndash;derived digital models remains limited. The present findings therefore extend existing knowledge by demonstrating that both BEWE and TWES 2.0 can be applied with acceptable reliability in a fully digital assessment environment [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Patterns of tooth wear\u003c/h2\u003e \u003cp\u003eFor BEWE, scores were higher overall, reflecting a broader scoring range and the tool\u0026rsquo;s sensitivity to more pronounced erosive wear. The distribution of scores confirms BEWE\u0026rsquo;s suitability for rapid screening, while TWES 2.0 provides detailed surface-specific assessment.\u003c/p\u003e \u003cp\u003eThe higher inter-rater reliability of BEWE is likely related to index design. BEWE relies on broader categorical thresholds and records only the most severely affected surface per sextant, which may reduce interpretative ambiguity and promote more consistent scoring. The higher inter-rater reliability observed for BEWE compared with TWES 2.0 is consistent with previous clinical validation studies, which have highlighted the simplicity of the BEWE scoring system as a key factor contributing to its reproducibility and examiner agreement [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous studies have emphasized that the simplified categorical structure of BEWE supports consistent scoring and facilitates reliable use in routine clinical settings, which may explain its higher inter-rater reliability compared with more detailed indices [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In contrast, TWES 2.0 provides a more detailed, surface-specific assessment, increasing diagnostic resolution but also examiner dependence, particularly when subtle wear changes are evaluated.\u003c/p\u003e \u003cp\u003eGreater variability was observed in posterior sextants, especially on occlusal surfaces, which may reflect anatomical complexity and less clearly defined transitions between wear grades. Greater variability observed on posterior and palatal surfaces has also been reported in previous studies and is commonly attributed to anatomical complexity, limited visual cues, and less distinct transitions between wear grades in these regions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Differences between raters were generally small and confined to adjacent score categories, suggesting inherent subjectivity of ordinal grading rather than systematic calibration bias. Additionally, the absence of color and texture information in intraoral scans may disproportionately affect more detailed indices such as TWES 2.0, whereas BEWE appears more robust in a purely digital environment.\u003c/p\u003e \u003cp\u003eTWES 2.0 scores were generally concentrated at the lower end of the scale (score 1), consistent with mild to moderate tooth wear in this adult population. Small but statistically significant differences between raters were observed in most sextants, with Rater 1 generally assigning slightly higher scores. These observations reflect minor inter-rater variability in grading subtle changes, particularly on posterior and palatal surfaces, without evidence of systematic bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Clinical implications\u003c/h2\u003e \u003cp\u003eWhen directly compared, BEWE demonstrated consistently higher inter-examiner reliability than the TWES 2.0 Screening Module across all sextants, as reflected by higher ICC values and overall Bland\u0026ndash;Altman agreement patterns. TWES 2.0 showed moderate to good reliability, with greater variability between examiners, particularly on posterior and palatal surfaces. These differences likely reflect the more detailed and surface-specific scoring structure of TWES 2.0, which may increase sensitivity to subtle wear features but also examiner dependence.\u003c/p\u003e \u003cp\u003eThe observed reliability of BEWE and TWES 2.0 underscores their utility as practical screening tools in both routine dental practice and large-scale epidemiological studies. BEWE\u0026rsquo;s simplicity and ease of use make it particularly suitable for rapid risk assessment of dental erosion, whereas TWES 2.0 allows a more detailed evaluation of wear severity and differentiation of etiological mechanisms (erosion, attrition, abrasion). The combination of these indices can guide treatment planning and preventive strategies, enabling early identification and management of patients at risk of progressive tooth wear.\u003c/p\u003e \u003cp\u003eImportantly, although statistically significant inter-rater differences were observed, these discrepancies were generally small and predominantly limited to adjacent score categories. From a clinical perspective, such differences are unlikely to result in changes in BEWE cumulative risk category classification, which forms the basis for clinical decision-making, including preventive counseling, monitoring intervals, and referral for restorative intervention.\u003c/p\u003e \u003cp\u003eConsequently, the observed level of inter-examiner variability is not expected to alter treatment thresholds or patient management strategies in routine practice. BEWE may therefore be reliably used as a primary screening tool to stratify patients according to erosion risk and determine appropriate recall intervals, while TWES 2.0 can complement this approach by providing more detailed, surface-specific information to support individualized treatment planning and longitudinal monitoring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations should be considered when interpreting the present findings. This study was retrospective and relied on anonymized intraoral scans, which limited the availability of patient-level data such as age beyond adulthood (\u0026ge;\u0026thinsp;18 years), dietary habits, oral hygiene behaviors, and parafunctional activity. In addition, sex information was unavailable for approximately 20% of the sample due to data anonymization.\u003c/p\u003e \u003cp\u003eMissing sextant-level data resulted from localized scan limitations, including incomplete capture, artifacts, or extensive restorative coverage preventing reliable scoring. Only paired sextant observations assessed by both examiners were included in the reliability analyses, leading to minor variations in sample size across sextants.\u003c/p\u003e \u003cp\u003eThe study focused exclusively on inter-rater reliability; intra-rater reliability was not assessed and should be addressed in future studies to further characterize examiner consistency over time. Furthermore, while intraclass correlation coefficients and Bland\u0026ndash;Altman plots were used to assess agreement, detailed numerical Bland\u0026ndash;Altman parameters (mean bias and limits of agreement) were not reported in detail and should be addressed in future studies.\u003c/p\u003e \u003cp\u003eFinally, all assessments were performed using digital intraoral scan\u0026ndash;derived models without color or texture information. Although identical software environments were used by both examiners, the absence of optical cues inherent to clinical examination may have influenced scoring, particularly for more detailed indices such as TWES 2.0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Future research directions\u003c/h2\u003e \u003cp\u003eFuture studies should aim to validate the application of BEWE and TWES 2.0 in prospective clinical settings, combining intraoral scan\u0026ndash;based assessment with direct chairside examination. Longitudinal studies using repeated intraoral scans would be particularly valuable to evaluate the ability of these indices to monitor tooth wear progression over time.\u003c/p\u003e \u003cp\u003eFurther research may also compare the performance of different intraoral scanners and visualization software platforms to determine whether hardware- or software-related factors influence scoring reliability. Finally, the integration of automated or artificial intelligence\u0026ndash;based wear detection tools may enhance objectivity and efficiency, potentially complementing established screening indices in digital workflows.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eBEWE and TWES 2.0 are reliable and practical tools for the assessment of noncarious tooth wear. Inter-rater reliability was generally good, with Bland\u0026ndash;Altman analyses showing that most differences between examiners were within acceptable limits and without evidence of systematic bias. While BEWE demonstrated slightly higher consistency across sextants, TWES 2.0 allows more detailed evaluation of wear severity, particularly on occlusal and palatal surfaces. These findings support the use of both indices in clinical practice and research for standardized monitoring, early detection, and risk assessment of tooth wear. Examiner calibration remains critical to ensure reproducibility, especially when using TWES 2.0.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to patient confidentiality and privacy restrictions, but anonymized summary data supporting the results are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003emanuscript preparation\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work, the author(s) used ChatGPT (OpenAI) to assist in refining sentence structure and clarity. After using this tool, the author(s) reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate -\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Bioethics Committee at the District Medical Chamber in Krak\u0026oacute;w, Poland (approval number: L.dz.OIL/KBL/22/2025, issued on 27 May 2025). Due to the retrospective and anonymized nature of the study, individual patient consent was not required; written permission to use the scans was provided by all three participating dental clinics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e - Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e - All data generated and analyzed during this study are included in this published article. The original intraoral scans are not publicly available due to ethical and privacy restrictions but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e - The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding -\u003c/strong\u003e This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions \u0026ndash;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eMaria Lorens\u003c/strong\u003e contributed to the conception and design of the review, conducted the literature search, organized and curated the data, prepared tables, and drafted the manuscript. She also participated in critical revisions, approved the final version, and accepts responsibility for the integrity of the work.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDr. Iwona Tomaszewska\u003c/strong\u003e contributed to the hypothesis development and design, supported the literature search and data interpretation, and critically revised the manuscript. She approved the final version and accepts responsibility for the integrity of the work.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll authors critically reviewed the manuscript and made significant intellectual contributions. Finally, all authors approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e - The authors would like to thank Biostat Research and Development for providing statistical analysis support for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAddy M, Shellis RP (2006) Interaction between attrition, abrasion and erosion in tooth wear. Monogr Oral Sci 20:17\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000093348\u003c/span\u003e\u003cspan address=\"10.1159/000093348\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez-Frias FJ, Castellanos-Cosano L, Martin-Gonzalez J, Llamas-Carreras JM, Segura-Egea JJ (2012) Clinical measurement of tooth wear: tooth Wear Indices. 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Monogr Oral Sci 20:32\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000093349\u003c/span\u003e\u003cspan address=\"10.1159/000093349\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Attrition, Basic Erosive Wear Examination (BEWE), Erosion, Tooth Wear, Tooth Wear Evaluation System 2.0 (TWES 2.0), Tooth Wear Index (TWI) Intraoral Scans, Inter-Rater Reliability","lastPublishedDoi":"10.21203/rs.3.rs-8502313/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8502313/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study assessed the reliability and clinical applicability of two tooth wear screening indices—Basic Erosive Wear Examination (BEWE) and the Tooth Wear Screening module of the Tooth Wear Evaluation System 2.0 (TWES 2.0)—using intraoral scans.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 246 anonymized intraoral scans from adult patients were independently evaluated by two calibrated examiners. Examiners calibration was performed prior to the study using a representative set of intraoral scans and a reference standard. Calibration was repeated until a predefined level of agreement was achieved before formal data collection. Scores for all sextants were recorded for BEWE and TWES 2.0. Inter-rater agreement was assessed using Wilcoxon signed-rank tests to detect systematic differences between paired scores, intraclass correlation coefficients (ICCs) to assess inter-examiner consistency, Bland–Altman plots to evaluate agreement between examiners. Statistical significance was set at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBEWE demonstrated good reliability, with ICCs ranging from 0.761 to 0.852 across sextants. According to commonly used ICC interpretation thresholds (poor \u0026lt; 0.50, moderate 0.50–0.75, good 0.75–0.90, excellent \u0026gt; 0.90), the observed ICCs indicate good inter-examiner reliability. Bland–Altman analysis showed small differences between examiners and no systematic bias. TWES 2.0 exhibited moderate to good reliability, with ICCs between 0.543 and 0.761.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth BEWE and TWES 2.0 are reliable and practical for screening noncarious tooth wear via intraoral scans. BEWE showed slightly higher inter-rater consistency, whereas TWES 2.0 allows more detailed evaluation of occlusal and palatal surfaces. These indices can support standardized monitoring, early detection, and clinical management of tooth wear. Examiner calibration remains essential, particularly for TWES 2.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Significance:\u003c/strong\u003e\u003cbr\u003e\nTooth wear is an increasingly prevalent condition in modern dentistry, often progressing silently until advanced stages. The application of BEWE and TWES 2.0 to intraoral scans provides clinicians with a standardized, noninvasive, and reproducible method for detecting and monitoring tooth wear at an early stage. Integrating these indices into routine digital workflows supports timely diagnosis, preventive management, and long-term follow-up of patients affected by erosive and attritional tooth wear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve an interventional clinical trial and therefore was not registered in a clinical trial registry. The study protocol was reviewed and approved by the Bioethics Committee at the District Medical Chamber in Kraków, Poland (approval number: L.dz.OIL/KBL/22/2025, issued on 27 May 2025)\u003c/p\u003e","manuscriptTitle":"A Reliability Assessment of the Basic Erosive Wear Examination and the Tooth Wear Evaluation System 2.0 Utilizing Intraoral Scan Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-08 14:55:14","doi":"10.21203/rs.3.rs-8502313/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fff6d7d4-2032-4362-a2e6-6d4849ca2ab2","owner":[],"postedDate":"January 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T08:41:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-08 14:55:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8502313","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8502313","identity":"rs-8502313","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}