Dentoalveolar Markers in A Group of Turkish Population with Different Anteroposterior Relationships: A CBCT Retrospective Study

Dentoalveolar Markers in A Group of Turkish Population with Different Anteroposterior Relationships: A CBCT Retrospective Study | 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 Dentoalveolar Markers in A Group of Turkish Population with Different Anteroposterior Relationships: A CBCT Retrospective Study Aslıhan Akbulut, Kübra Akkın, Ayşen Gürsoy, Kaan Orhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8202041/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Objective: To assess collum angle and alveolar bone thickness across different anteroposterior incisor relationships using cone-beam computed tomography (CBCT), and to analyze the correlations between these morphological parameters. Materials and Methods: This retrospective CBCT study included 200 Turkish patients (12–37 years) categorized according to the British Standards Institution (BSI) incisor classification: Class I, Class II/1, Class II/2, and Class III. Collum angle and buccal, palatal, and lingual alveolar bone thicknesses were measured on maxillary and mandibular anterior teeth and premolars at 5 mm apical to the CEJ using standardized sagittal slices. Results: Collum angle varied significantly among malocclusion groups, with the greatest values observed in Class II/2 and Class III individuals. Mandibular teeth consistently exhibited positive collum angles, whereas maxillary teeth showed class-dependent variation. Buccal bone thickness was substantially reduced in Class III mandibular incisors and canines, while Class II/2 subjects presented increased palatal thickness in selected maxillary teeth. Correlation analysis revealed tooth- and class-specific associations, including a moderate negative correlation between collum angle and buccal thickness in Class II/2 maxillary central incisors, and a strong positive correlation with lingual thickness in Class III mandibular lateral incisors. Conclusion: Collum angle and alveolar bone thickness exhibit distinct, class-dependent morphological patterns that may restrict the biomechanical limits of orthodontic tooth movement. The pronounced root inclinations and reduced alveolar support in Class II/2 and Class III patients underscore the need for detailed CBCT evaluation to prevent periodontal complications during treatment. CBCT collum angle alveolar bone thickness malocclusion orthodontics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The positional relationship between maxillary and mandibular incisors and their surrounding alveolar bone is a fundamental consideration in orthodontic diagnosis and treatment planning. In individuals with varying skeletal patterns especially those with severe discrepancies or requiring orthodontic-surgical intervention orthodontic tooth movement is inherently restricted by alveolar bone morphology and periodontal condition ( 1 , 2 ). The collum angle, defined as the angle between the long axis of the crown and that of the root, plays a key role in determining the degree of lingual root torque in anterior teeth. Significant increases or decreases in the collum angle may predispose roots to contact with labial or lingual cortical plates, altering force distribution and increasing mechanical stress on supporting structures during functional loading ( 3 , 4 ). Variations in collum angle and alveolar bone thickness have substantial implications for orthodontic treatment planning, underscoring the importance of pre-treatment CBCT imaging to reduce the risk of complications such as dehiscence and fenestration ( 5 ). Clinically, torque control is essential as it influences tooth inclination, occlusal force distribution, and the long-term stability of orthodontic outcomes. Lee et al. ( 6 ) reported that differences in labial crown morphology and collum angle significantly affect torque expression and root positioning in maxillary anterior teeth, emphasizing the need for individualized CBCT assessment. Furthermore, clinical considerations of torque are critical for ensuring proper tooth inclination, occlusal load distribution, and long-term orthodontic stability ( 6 ). In addition, crown–root angulation and buccolingual alveolar bone thickness represent critical anatomical boundaries that must be respected during safe tooth movement. Failure to consider such factors may result in iatrogenic complications, including external apical root resorption, bone dehiscence, fenestration, gingival recession, and attachment loss ( 7 , 8 ). Thin buccolingual alveolar bone or pronounced collum angles increase the risk of cortical plate perforation and reduced periodontal support. Prior CBCT studies have documented the development or progression of dehiscence during orthodontic treatment, particularly on the buccal surfaces of anterior teeth ( 9 , 10 ). Root displacement beyond the alveolar housing may lead to root exposure, mobility, soft tissue recession, and, in severe cases, tooth loss ( 8 , 10 ). Therefore, inadequate attention to collum angle and alveolar bone thickness during tooth movement may compromise root integrity and periodontal health, increasing relapse risk and jeopardizing long-term stability ( 8 ). Previous research has indicated that both the collum angle and buccolingual alveolar bone thickness vary depending on anteroposterior dental and skeletal patterns. Issrani et al. ( 11 ) demonstrated that individuals with Class II Division 2 malocclusion exhibit a significantly higher collum angle compared with other groups, although no significant differences were observed between ethnic cohorts. Ma et al. ( 12 ) reported reduced anterior alveolar bone thickness in patients with severe skeletal Class III malocclusion compared with those with severe skeletal Class II malocclusion. Additionally, the enlarged collum angle commonly observed in Class II Division 2 malocclusion may complicate torque and intrusion mechanics, increasing the risk of palatal cortical plate perforation ( 11 ). The present study aims to investigate variations in collum angle and buccopalatal/lingual alveolar bone thickness in incisors and premolars across different anteroposterior malocclusion groups, considering age, sex, and skeletal classification. Furthermore, we sought to evaluate the correlation between collum angle and alveolar bone thickness in these populations. 2. Materials and Methods 2.1 Study Design This retrospective, single-center study was conducted at the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Bolu Abant İzzet Baysal University. The study protocol was approved by the Ethics Committee of Istanbul Medipol University and adhered to the ethical principles outlined in the Declaration of Helsinki. Ethical approval was granted on January 7, 2025, under approval number E-10840098-202.3.02-974. The need for informed consent was waived due to the use of archived CBCT images collected for diagnostic purposes. This observational study was designed and reported in accordance with the STROBE reporting guidelines (von Elm et al., PLoS Med 2007). CBCT imaging was justified according to the radiation exposure recommendations of the Hargreaves and American Association of Endodontists (AAE) guidelines to ensure appropriate indication. 2.2. Study Population CBCT records were retrospectively retrieved from the digital archives of the Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Bolu Abant Izzet Baysal University, covering the period from January 2020 to October 2024. A total of 200 Turkish patients (age range: 12–37 years) with different anteroposterior skeletal relationships were included in this study. Demographic and clinical characteristics of the participants are summarized in Table 1 . Table 1 Characteristics of patients participating in the study f % Gender Female 130 65 Male 70 35 Age Group <= 18 69 34.5 19–24 91 45.5 25+ 40 20 Class Class 1 149 74.5 2div1 18 9 2div2 21 10.5 Class 3 12 6 Total 200 100 2.3. Sample Size Calculation The sample size was calculated a priori using G*Power (version 3.1.9.7), based on assumptions drawn from previous literature to ensure adequate statistical power. A power of 0.80 (80%), type I error (α) of 0.05, and type II error (β) of 0.20 were selected (Schober & Vetter, 2019; Kang, 2021; Riesthuis, 2024). A minimum of 128 subjects was required to detect a medium effect size (Cohen’s d = 0.5) for independent samples t-tests. Additionally, for one-way ANOVA involving four groups the maximum number of malocclusion classes assessed a minimum of 180 subjects was needed to detect a medium effect size (f = 0.25). Accordingly, a total sample size of 200 participants was deemed sufficient. 2.4. Inclusion/ Exclusion Criteria The study included patients aged between 12 and 37 years who underwent CBCT imaging as part of orthodontic treatment planning. Inclusion criteria were as follows: presence of fully erupted incisors and premolars in both arches, no history of orthodontic treatment, and availability of high-resolution CBCT images suitable for precise measurement of the collum angle and alveolar bone dimensions. The patients were categorized according to the British Standards Institution (BSI) incisor classification: Class I, Class II/1, Class II/2, and Class III. Exclusion criteria included individuals with a history of craniofacial syndromes or anomalies, previous trauma or surgery in the anterior region, periodontal disease affecting anterior teeth, missing anterior teeth, restorations that could obscure anatomical landmarks, or poor CBCT image quality. Additionally, patients with active pathological lesions in the maxillofacial region or incomplete medical records were excluded. After applying these criteria, 20 of the initial 220 CBCT scans were excluded due to insufficient image quality, and the final sample selection is illustrated in the flow diagram (Fig. 1 ). 2.5. CBCT Acquisition and Image Analysis CBCT images were acquired at the Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Bolu Abant Izzet Baysal University, using the i-CAT 3D Imaging System (Imaging Sciences International, Hatfield, PA, USA) equipped with an amorphous silicon flat-panel detector (23.8 cm × 19.2 cm). Scans were obtained under standardized exposure settings (FOV: 16 × 9 cm; voxel size: 0.3 mm; 5 mA; 120 kVp; 8.9 s). Patients were seated with head support, and laser guide lines were used to standardize positioning by aligning the vertical beam with the facial midline and the horizontal beam with the lip commissures. All datasets were imported into i-CAT Vision software for initial orientation, after which measurements were performed using OnDemand3D (Cybermed Inc., Seoul, South Korea) and Dolphin Imaging (Dolphin Imaging & Management Solutions, Chatsworth, CA, USA). Head orientation was refined in the axial, sagittal, and coronal planes to approximate natural head posture before analysis. A single calibrated dentomaxillofacial radiologist (A.A.), with 18 years of CBCT experience, performed all measurements twice with a 2-week. Intra-observer reliability was high (ICC = 0.92; 95% CI: 0.88–0.96). When the two repeated measurements differed by more than 2°, a third measurement was performed and the final value was calculated as the average of the three measurements.Images were evaluated under dim lighting, and window settings were adjusted when necessary to improve visualization. To assess intra-observer variation, 30 images were re-measured two weeks apart. If the difference between the two measurements exceeded 2°, a third measurement was performed, and the final value was recorded as the average of the three measurements. This study evaluated collum angles and buccal, lingual, and palatal alveolar bone thicknesses—measured 5 mm apical to the cementoenamel junction (CEJ) across Class I, Class II Division 1, Class II Division 2, and Class III malocclusion groups. Collum Angle (°) The oblique slicing method was used to measure the collum angle. Three parasagittal planes were generated for each tooth: one through the mesiodistal midpoint, one through the buccolingual midpoint, and one at the cemento-enamel junction (CEJ). The angle was defined between the crown's extended long axis and the root's long axis. A positive value was recorded when the crown axis extended labially relative to the root axis. Zero was recorded when both axes were aligned. A negative value was recorded when the crown axis extended lingually. Crown axis ran from incisal midpoint to CEJ center, while root axis extended to apex. (Fıgure 2) For premolars, the crown axis ran from the central groove to the CEJ midpoint, and the root axis from this point to the midpoint between buccal and palatal root apices ( 13 ). Alveolar Bone Thickness (mm) Alveolar bone thickness was measured on sagittal CBCT sections through the mesiodistal midpoint of each tooth. Incisors and Canines: Buccal and palatal bone thickness were measured 5 mm apical to the CEJ as the distance from the outer cortical plate to the corresponding tooth surface (Fig. 3 ). Maxillary Premolars: Buccal bone thickness was measured from the buccal cortical plate to the buccal root surface, and palatal bone thickness from the palatal plate to the palatal root. Mandibular Premolars: Buccal and lingual bone thicknesses were measured using the same method as mandibular anterior teeth (Fig. 4 ). 3. Results Collum Angle Analysis Mandibular anterior teeth exhibited consistently positive collum angles across all malocclusion groups, reflecting a stable crown–root inclination pattern. In contrast, maxillary teeth showed marked intergroup variability, particularly in the central incisors, where the angulation pattern was strongly influenced by the sagittal incisor relationship. A statistically significant difference was detected in the upper left central incisor (tooth 21), where Class II Division 2 presented the greatest positive collum angle, followed by Class III, while Class II Division 1 exhibited reduced or negative values (p CI and CII/1, and CIII > CI. In the mandibular dentition, significant intergroup variation occurred only in the lower left lateral incisor (tooth 32) (p < 0.05). Class III patients had the highest positive angles, followed by Class II Division 1, whereas Class II Division 2 had the lowest values. Post-hoc tests showed CI CII/2. No other maxillary or mandibular teeth exhibited statistically meaningful differences. Overall, collum angle morphology showed the greatest deviations in CII/2 and CIII groups, likely representing dentoalveolar compensations to sagittal skeletal discrepancy. Table 2 presents the mean collum angle values and standard deviations for each tooth across malocclusion groups. Table 2 Interclass Comparison of Collum Angle Measurements Across Maxillary and Mandibular Teeth CI CII/1 CII/2 CIII (N = 149) (N = 18) (N = 21) (N = 12) Tooth group Tooth Mean SD Mean SD Mean SD Mean SD p Sig η²p Post-Hoc (LSD) Lower central incisor L1 31 3.16 4.85 2.83 4.87 3.71 4.84 3.5 4.19 0.94 0.002 41 2.8 4.94 2.67 5.22 3.19 4.49 2.92 4.32 0.986 0.001 Lower lateral incisor L2 32 4.36 4.38 6.67 6.13 3.76 3.83 7.42 4.38 0.025 ** 0.046 CI CII/2 42 4.72 4.5 5.78 5.73 4.24 4.41 7.58 6.22 0.169 0.025 Lower canine L3 33 6.7 5.57 7 5.49 7.81 5.84 6.08 4.06 0.808 0.005 43 6.46 5.71 6.72 5.82 8.14 4.68 6.5 5.58 0.644 0.008 Lower first premolar L4 34 22.32 8.58 20.22 5.53 25.14 6.55 18.42 6.89 0.092 0.032 44 22.56 7.26 21.56 7.76 23 6.96 22.92 5.76 0.928 0.002 Lower Second premolar L5 35 14.55 7.4 12.78 6.17 14.19 5.82 14.08 5.7 0.793 0.005 45 15.25 6.91 14.17 7.88 13.38 8.49 14.75 6.7 0.686 0.008 Upper central incisor U1 11 -0.02 5.1 0.17 7.25 1.95 4.7 2.83 3.61 0.144 0.027 21 -0.65 4.72 -2.67 7.08 1.81 4.23 1.25 4.99 0.024 ** 0.047 CII/2 > CI and CII/1, CIII > CII/1 Upper Lateral incisor U2 12 2.54 4.93 2.33 6.68 3.24 5.53 4 4.99 0.748 0.006 22 3.47 4.72 2.39 6.16 3.62 7.07 5.17 6.74 0.57 0.01 Upper canine U3 13 -2.99 5.31 -2.67 8.67 -4.52 5.33 -3.33 4.01 0.677 0.008 23 -2.68 5.29 -4.44 6.59 -1.81 6.19 -3.08 7.42 0.521 0.011 Upper first premolar U4 14 6.64 6.06 6.67 6.98 7.95 6.05 8.83 7.22 0.562 0.01 24 7.35 6.07 4.33 6.76 9.38 9.07 5.5 5.63 0.082 0.034 Upper second premolar U5 15 2.35 6.2 1.5 5.25 2.67 6.06 3.08 3.7 0.896 0.003 25 1.73 6.18 0 3.69 2.52 8.6 2.5 7.61 0.613 0.009 ** Significant at P < 0.05, *** Significant at P < 0.01 η²p (effect size): 0.01 = small, 0.06 = medium, 0.14 = large. P-values were adjusted using the Bonferroni correction. Gender-Based Class Comparison of Collum Angles When collum angle values were analyzed separately for males and females, no significant interclass differences were found in the lower central incisors, lower canines, lower first premolars, or lower second premolars for either gender (p > 0.05). In the lower lateral incisors, different patterns emerged by gender. For tooth 32, no significant difference was observed among female classes (p > 0.05), whereas males showed a significant interclass difference (p < 0.05). Post-hoc analysis indicated that Class III males had the highest collum angles, Class II/1 males were higher than all groups except Class III, and Class II/2 males exhibited the lowest values. For tooth 42, the opposite pattern was found: female classes differed significantly (p 0.05). In the maxillary teeth (central incisors, lateral incisors, canines, first and second premolars), no significant interclass differences were detected in either gender (p > 0.05). Table 3 values are presented as mean ± standard deviation. P-values refer to one-way ANOVA performed separately for males and females. Effect size is reported as partial eta-squared (η²p). Post-hoc comparisons were carried out using LSD. Table 3 Gender-Specific Interclass Comparison of Collum Angle Measurements CI (F/M) CII/1 (F/M) CII/2 (F/M) CIII (F/M) (F/M) (F/M) (F/M) (N = 99/50) (N = 13/5) (N = 12/9) (N = 6/6) Tooth group Tooth Mean ± SD Mean ± SD Mean ± SD Mean ± SD P Sig η²p Post-Hoc (LSD) Lower central incisor L1 31 2.74 ± 4.9 /4 ± 4.67 2 ± 4.8 /5 ± 4.85 3.67 ± 5.14 /3.78 ± 4.71 2.67 ± 2.94/ 4.33 ± 5.32 0.86/0.97 -/- 0.006/0.004 - 41 2.68 ± 4.84 /3.04 ± 5.17 2.38 ± 5.19/3.4 ± 5.86 2.33 ± 2.93/ 4.33 ± 6 3.83 ± 5.81/ 2 ± 2.28 0.93/0.85 -/- 0.004/0.012 - Lower lateral incisor L2 32 4.11 ± 4.24 /4.86 ± 4.65 4.69 ± 5.28 /11.8 ± 5.5 4.17 ± 3.33 /3.22 ± 4.58 7.5 ± 5.01 /7.33 ± 4.13 0.31/0.01 -/*** 0.028/0.166 Male : CII/1 > CI; CII/2 < CII/1; CII/1 CII/2; CIII > CI; CIII > CII/2 42 4.48 ± 4.43/ 5.2 ± 4.65 4.15 ± 4.79 /10 ± 6.32 3.92 ± 4.78/ 4.67 ± 4.12 10 ± 6.78/5.17 ± 5 0.04/ 0.18 **/- 0.064/0.071 Female : CII/1 > CI; CII/1 CII/2 Lower canine L3 33 6.66 ± 5.57/ 6.8 ± 5.62 7 ± 6.03/ 7 ± 4/36 8/42 ± 4.4/ 7 ± 7.58 7.17 ± 3.87/ 5 ± 4/29 0.77/0.9 -/- 0.009/0.009 - 43 6.57 ± 5.64/ 6.24 ± 5.92 6.92 ± 6.65/ 6.2 ± 3.37 8.58 ± 4.87/ 7.56 ± 4.64 8 ± 4.47/ 6.57 ± 0.86 0.65/0.86 -/- 0.013/ 0.011 - Lower first premolar L4 34 22.65 ± 8.26/ 21.68 ± 9.22 19.54 ± 6.05/ 22 ± 3.81 24.33 ± 6.61/ 26.22 ± 6.7 19.33 ± 7.42/ 17.5 ± 6.89 0.34/ 0.28 -/- 0.026/ 0.056 - 44 22.4 ± 7.99/ 21.98 ± 5.56 20.46 ± 8.23/ 24.4 ± 4.45 24.17 ± 8.29/ 21.44 ± 4.69 24.83 ± 4.45/ 21 ± 6.66 0.6/ 0.75 -/- 0.015/ 0.018 - Lower Second premolar L5 35 14.06 ± 7.96/ 15.52 ± 6.11 12.15 ± 6.24/ 14.4 ± 6.35 13.67 ± 5.3/ 14.89 ± 6.72 14 ± 7.72/ 14.17 ± 3.43 0.87/0/93 -/- 0.006/0.006 - 45 14.6 ± 77.08/16.4 ± 6.49 15.46 ± 7.31/ 10.8 ± 9.15 11.58 ± 9.25/ 15.78 ± 7.17 15.17 ± 6.88/ 14.33 ± 7.15 0.53/0.35 -/- 0.017/0.048 - Upper central incisor U1 11 0.22 ± 4.67/ -0.5 ± 5.88 0.54 ± 7.5/-0.8 ± 7.26 0.75 ± 5.41/3.56 ± 3.13 2 ± 3.63/3.67 ± 3.72 0.85/0.1 -/- 0.006/0.089 - 21 -0.29 ± 4.75/ -1.36 ± 4.63 -2.62 ± 8.09/ -2.8 ± 4.09 1.42 ± 3.85/2.33 ± 4.87 1 ± 6.13/1.5 ± 4.14 0.23/0.07 -/- 0.033/0.101 - Upper Lateral incisor U2 12 2.59 ± 5.2 /2.44 ± 4.39 2.15 ± 7.79/ 2.8 ± 2.68 5.17 ± 6.06/ 0.67 ± 3.61 4.17 ± 6.71/3.83 ± 3.13 0.43/0.51 -/- 0.021/0.034 - 22 3.41 ± 4.74/3.58 ± 4.72 2.92 ± 6.85/1 ± 4.12 5 ± 5.12/ 1.78 ± 9.07 4.17 ± 7.96/6.17 ± 5.85 0.73/0.35 -/- 0.01/0.048 - Upper canine U3 13 -2.89 ± 5.54/-3.18 ± 4.86 -4.38 ± 8/1.8 ± 9.65 -3 ± 4.07/-6.56 ± 6.33 -3.67 ± 3.44/-3 ± 4.82 0.83/0.06 -/- 0.007/0.103 - 23 -2.69 ± 5.6/ -2.68 ± 4.66 -6.31 ± 5.06/0.4 ± 8.2 -1.75 ± 6.3/-1.89 ± 6.43 -5.33 ± 5.5 /-0.83 ± .86 0.1/0.61 -/- 0.049/ 0.027 - Upper first premolar U4 14 5.95 ± 6.38/ 8 ± 5.14 5.54 ± 7.05/9.6 ± 6.54 6.67 ± 6.02/9.67 ± 5.98 8.83 ± 10.268.83 ± 3.06 0.74/0.77 -/- 0.01/0.017 . 24 7.03 ± 6.43/ 7.98 ± 5.28 5.69 ± 6.41/ 0.8 ± 7.01 9.5 ± 8.79/ 9.22 ± 9.98 5.83 ± 5.81/ 5.17 ± 5.98 0.5/0.06 -/- 0.019/0.104 - Upper second premolar U5 15 1.55 ± 6.06/ 3.94 ± 6.22 0.85 ± 5.18/3.2 ± 5.63 2.17 ± 7.72/3.33 ± 3 3.33 ± 4.08/2.83 ± 3.66 0.85/0.96 -/- 0.006/0.005 - 25 1.38 ± 6.51/2.42 ± 5.47 1.15 ± 2.85/-3 ± 4.24 2.42 ± 9.3/2.67 ± 8.12 -1.67 ± 5.68/6.67 ± 7.31 0.66/0.07 -/- 0.013/0.099 - ** Significant at P < 0.05, *** Significant at P < 0.01 p < 0.05. F = Female, M = Male. Buccal, Lingual, and Palatal Alveolar Bone Thickness Three-dimensional measurements of cortical bone thickness revealed class-dependent patterns that varied across jaw regions and surfaces (Table 4 ). Buccal Thickness Significant differences were observed primarily in the maxillary lateral incisor (U2–22), where Class I exhibited thicker buccal bone compared with Class II/2 and Class III (p < 0.05). Buccal cortical thinning in retroclined Class II/2 incisors and protrusive Class III incisors is consistent with their morphological compensation patterns. Lingual Thickness Lingual cortical bone was significantly different in the mandibular lateral incisors (32 and 42) and first premolar (34). Class II/2 consistently demonstrated greater lingual thickness, suggesting a posterior positional adaptation that may protect against dehiscence despite exaggerated collum angle morphology. Palatal Thickness Significant palatal differences were found in the maxillary canine ( 23 ) and second premolar ( 15 ), where Class II/2 once again exhibited the highest thickness. These results may reflect the palatally displaced root axes common in Class II/2 profiles. Table 4. Buccal, Lingual, and Palatal Alveolar Bone Thickness Across Malocclusion Classes Age-stratified analysis showed that collum angle differences between classes became evident only after adulthood. In participants ≤ 18 years, no significant interclass differences were detected for any tooth. However, in the ≥ 19-year group, clear distinctions emerged: For tooth 21, Class II/2 and Class III exhibited significantly higher collum angles than Class I and Class II/1 (p < 0.05). For tooth 13, Class II/1 showed the highest values, whereas Class II/2 and Class III presented the lowest. These findings indicate that class-related variations in collum angle morphology become more pronounced after growth completion. Correlation Between Collum Angle and Alveolar Bone Thickness Several tooth- and class-specific correlations were identified between the collum angle and cortical bone thickness. In the upper central incisors, Class II/2 patients showed a moderate negative correlation between collum angle and buccal thickness for tooth 11 (r = − 0.456, p < 0.05), while Class I exhibited a weak negative correlation between collum angle and palatal thickness for tooth 21 (r = − 0.204, p < 0.05). A similar weak negative correlation with palatal thickness was noted for tooth 22 in Class I (r = − 0.189, p < 0.05). For the upper first premolars, buccal thickness was negatively correlated with collum angle in both Class I (r = − 0.211, p < 0.05) and Class III (r = − 0.739, p < 0.05), with a stronger effect in Class III. In the mandibular incisors, Class I showed a weak negative correlation between collum angle and buccal thickness for tooth 41 (r = − 0.256, p < 0.05). In contrast, a strong positive correlation was observed between collum angle and lingual thickness in Class III for tooth 42 (r = 0.615, p < 0.05). A weak negative correlation was also present in the lower canine 43 for Class I (r = − 0.241, p < 0.05). Overall, increasing collum angle was generally associated with reduced buccal or palatal cortical thickness, except for the lower lateral incisor in Class III patients, where lingual thickness increased with greater angulation (Fig. 5). Figure 5. Heatmap illustrating the relationship between collum angle and buccal, palatal, and lingual cortical bone thickness across different tooth types and malocclusion classes. Buccal, lingual, and palatal cortical measurements demonstrated several class- and gender-specific differences. Buccal thickness differences were more prominent in males, particularly in teeth such as L1–41, L2–42, U1–21, U2–12, U2–22, and U3–13, where Class II/2 generally showed lower values and Class I exhibited higher measurements, especially in the maxillary anterior region. In contrast, Class II/1 males displayed higher buccal thickness in the lower incisor region (L1, L2). Lingual thickness differences were most notable in the lower incisors and canines (L1, L2, L3). Class II/2 demonstrated the highest lingual measurements—particularly in females—while Class III consistently showed the lowest values. Although Class II/2 had higher averages in L4 and L5, these differences were not statistically significant. For palatal measurements, significant differences were tooth-specific. Class II/2 exhibited higher palatal thickness in U1–21 (females) and U3–23 (females). In U2–22 (males), the Class I group had the highest values. Significant differences in the upper posterior region were limited; only U5–15 in females showed higher palatal thickness in Class II/1 and Class II/2. No other palatal surfaces demonstrated statistically meaningful variation. Statistical Analysis Statistical analyses were performed using SPSS 27 (IBM Corp., Armonk, NY, USA). Data visualization (boxplots and heatmaps) was conducted in RStudio (version 4.5.1) using the ggplot2 and ggpubr packages. Descriptive statistics were reported as frequencies, percentages, means, and standard deviations. Normality was assessed using skewness and kurtosis values, which fell within the ± 2 range; therefore, parametric tests were applied. Group comparisons by gender were performed using independent samples t-tests, and effect sizes were reported as Cohen’s d. One-way ANOVA was used to compare collum angle and bone thickness measurements across age and malocclusion classes. Homogeneity of variances was evaluated using Levene’s test. When ANOVA showed significance, pairwise comparisons were conducted using the LSD post-hoc test. Pearson correlation analysis was used to examine relationships between collum angle and alveolar bone thickness. Statistical significance was set at p < 0.05. 4. Discussion This study comprehensively evaluated collum angle morphology and alveolar bone thickness across different anteroposterior incisor relationships using CBCT. Understanding these parameters is essential, as tooth inclination and its position within the alveolar housing directly affect torque control, periodontal risk, and the biological limits of orthodontic tooth movement ( 1 , 15 ). Unlike previous research, which often focused solely on anterior teeth or assessed bone and collum angle independently ( 16 – 20 ), the present investigation examined both parameters simultaneously and across a broader range of teeth, thereby providing a more complete morphological profile. Consistent with earlier findings ( 1 – 4 ), mandibular teeth exhibited predominantly positive collum angles, suggesting a lingual root inclination that may support functional loading and occlusal adaptation. In contrast, maxillary teeth displayed class-dependent variations: Class II Division 2 and Class III subjects demonstrated significantly greater positive collum angles in the maxillary central incisors, indicating a tendency for the roots to approach the palatal cortical plate. This aligns with Issrani et al. ( 11 ) and Rawaqa et al. ( 5 ), who emphasized the increased palatal root positioning and restricted alveolar envelope in these malocclusion groups. Clinically, these anatomical features heighten the risk of cortical perforation during torque or intrusion movements, consistent with concerns raised by Handelman ( 23 ) and Guo et al. ( 24 ). Alveolar bone thickness also varied according to malocclusion class. Class I individuals generally exhibited thicker buccal and palatal plates in the upper anterior region, whereas Class III subjects demonstrated reduced alveolar support, particularly buccally, supporting previous reports by Al-Masri et al. ( 2 ) and Lee et al. ( 6 ). These findings suggest that the alveolar housing in Class III individuals provides a narrower safe zone for labial or lingual movements. The increased palatal thickness in Class II subjects, especially in the maxillary canines and premolars, may represent a compensatory morphological adaptation consistent with observations of Rawaqa et al. ( 5 ). Correlation analysis revealed mostly negative associations between collum angle and cortical thickness, particularly in the maxillary incisors of Class II Division 2 subjects—indicating that increasing root inclination coincides with reduced buccal or palatal bone support. Similar patterns of vulnerability have been reported in finite element and CBCT-based studies linking excessive inclination to cortical thinning and dehiscence formation ( 8 – 10 ). Conversely, the positive correlation in the mandibular lateral incisors of Class III subjects suggests an adaptive lingual reinforcement that may help maintain periodontal stability. Overall, these findings highlight that variations in collum angle and alveolar bone morphology are strongly malocclusion-specific and clinically relevant. Root inclination directly influences the available alveolar envelope and may increase the risk of periodontal complications if orthodontic mechanics exceed the biological boundaries of the cortical plates ( 7 , 25 ). Therefore, individualized torque planning is essential, particularly in Class II/2 and Class III cases, where anatomical constraints are more pronounced. LIMITATIONS AND FUTURE DIRECTIONS LIMITATIONS AND FUTURE DIRECTIONS This study presents certain limitations that should be considered when interpreting the findings. The sample distribution across age, gender, and malocclusion groups was not fully balanced, which may have affected the robustness of subgroup comparisons. Additionally, the analysis relied solely on static CBCT-derived morphological parameters; dynamic changes that occur during active orthodontic tooth movement were not assessed. Malocclusion classification was based exclusively on BSI incisor relationships obtained from CBCT images, without incorporating soft-tissue features such as facial profile or lip dynamics, which may have provided a more comprehensive understanding of sagittal discrepancies. Although all measurements were performed manually, recent advances in artificial-intelligence–based automated segmentation have demonstrated increasing accuracy and consistency in CBCT morphological analysis ( 26 ). Incorporating such AI-assisted tools in future studies may help reduce observer-dependent variability and improve analytical efficiency. Furthermore, although longitudinal CBCT imaging was not available for this cohort, ethically justified follow-up imaging would help determine whether collum angle or alveolar bone morphology undergo clinically meaningful changes over time, thereby enhancing the understanding of dentoalveolar adaptation. Future research should aim to include larger and more balanced samples, integrate soft-tissue assessments, and combine CBCT-derived morphology with biomechanical approaches such as finite element modeling. Such comprehensive analyses would deepen insights into stress distribution patterns, cortical boundary limitations, and individualized orthodontic treatment planning. CLINICAL IMPLICATIONS The findings underscore the need for individualized torque and movement planning, particularly for Class II Division 2 and Class III patients, who demonstrated high collum angles and reduced alveolar support. These anatomical constraints increase susceptibility to dehiscence, fenestration, and periodontal compromise during excessive torque or intrusion movements. CBCT-based assessment should therefore be considered in cases with suspected limited alveolar housing to prevent iatrogenic sequelae and to optimize orthodontic biomechanics. Conclusion Mandibular teeth generally exhibited positive collum angles, while maxillary teeth demonstrated class-dependent root inclinations, with the greatest values observed in Class II Division 2 and Class III individuals. Alveolar bone thickness also varied significantly across classes, with Class III subjects showing reduced buccal support and Class II subjects demonstrating increased palatal thickness in several tooth groups. Significant correlations between collum angle and cortical thickness highlight the biomechanical relevance of root inclination in determining the limits of safe tooth movement. These findings emphasize the importance of integrating detailed CBCT-based morphology into orthodontic diagnosis and treatment planning to ensure biologically compatible tooth movement and to minimize periodontal risks. Declarations Ethics Approval and Consent to Participate: This study was approved by the Istanbul Medipol University Research Ethics Committee (Approval No: E-10840098-202.3.02-974). Competing Interests: The authors declare no competing interests. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Availability: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. 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15:07:05","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6949,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/7bc98fb14d2f8f04ec800f8b.png"},{"id":100547237,"identity":"8e653ee4-9b50-4e5c-9388-473662009219","added_by":"auto","created_at":"2026-01-19 08:14:57","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25592,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/7d4aa5a6ee3a58475348392d.png"},{"id":100432806,"identity":"badd00f5-3799-4225-8c93-06ffe1824c5f","added_by":"auto","created_at":"2026-01-16 15:07:05","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12227,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/4ddb62653fe99ddeae7c493b.png"},{"id":100546909,"identity":"4b382362-1223-4983-8fc1-7161b89da7ed","added_by":"auto","created_at":"2026-01-19 08:13:08","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8742,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/8cf8edccab787b0268370754.png"},{"id":100547007,"identity":"318779b7-ff52-41d5-a31d-10bafa36e472","added_by":"auto","created_at":"2026-01-19 08:13:55","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19332,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/580bd163c9566e9fbe85c817.png"},{"id":100546541,"identity":"bd50d23b-7b88-4a04-91d1-de14d7768c98","added_by":"auto","created_at":"2026-01-19 08:10:28","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13385,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/f2673fad788c878ed24d316b.png"},{"id":100432815,"identity":"468ec721-d6e2-4d91-a9f5-8e84bf5afd7d","added_by":"auto","created_at":"2026-01-16 15:07:05","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":254578,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/93ff6081fb5ed3d1c5c6dbda.png"},{"id":100432817,"identity":"d798d70e-f13a-4dcd-bac9-eb08b3b837f4","added_by":"auto","created_at":"2026-01-16 15:07:06","extension":"xml","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":158071,"visible":true,"origin":"","legend":"","description":"","filename":"7890a8c9b4a142e5b5200eb366c8c4561structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/7a742f935c5158b4690472ec.xml"},{"id":100432816,"identity":"4caf9f95-f5f5-4b67-a14a-bf414a7b209e","added_by":"auto","created_at":"2026-01-16 15:07:05","extension":"html","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":174675,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/11b594c1b0aa4a2a91e7e9eb.html"},{"id":100432778,"identity":"c097a806-2bd5-4226-beed-79202ca6714f","added_by":"auto","created_at":"2026-01-16 15:07:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46085,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA-style flow diagram showing the exclusion of low-quality CBCT scans.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/4e4f1aaff92e4f1ce8302c61.png"},{"id":100546519,"identity":"78676ad4-6362-470d-bbe9-b432e465d367","added_by":"auto","created_at":"2026-01-19 08:10:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":176719,"visible":true,"origin":"","legend":"\u003cp\u003eCollum angle measurements for the upper anterior central tooth using CEJ, root long axis, and crown long axis as guides. The angle between the two blue lines represents the collum angle formed between the long axis of the crown and the long axis of the root.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/21521cfaf8cde0a272b9052b.png"},{"id":100546783,"identity":"db8d6641-ee30-4d8e-b2d8-4b0fba898efb","added_by":"auto","created_at":"2026-01-19 08:12:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":262401,"visible":true,"origin":"","legend":"\u003cp\u003eBone width measurements for upper anterior tooth. On a line made parallel to the CEJs at 5mm distance, bone width was measured between the buccal/lingual most point on buccal/lingual bone and the outermost buccal/lingual surface of the root.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/049aa8c8c39484cf3af378a2.png"},{"id":100432782,"identity":"1d87b535-016c-49c7-8a97-99d70d9ae6de","added_by":"auto","created_at":"2026-01-16 15:07:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":271224,"visible":true,"origin":"","legend":"\u003cp\u003eBone width measurements for upper premolar tooth. On a line made parallel to the CEJs at 5mm distance, bone width was measured between the buccal/lingual most point on buccal/lingual bone and the outermost buccal/lingual surface of the root. The green line show that CEJ. The red line indicates the buccal and palatal bone length at a distance of 5 mm from the cementoenamel junction (CEJ).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/0cf61b41f0438867f350bcf8.png"},{"id":100546346,"identity":"e9f24266-c06f-4cdd-b717-54b0ed8a8076","added_by":"auto","created_at":"2026-01-19 08:06:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":152010,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap illustrating the relationship between collum angle and buccal, palatal, and lingual cortical bone thickness across different tooth types and malocclusion classes.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/71450131ff5587112c0ba414.png"},{"id":100554109,"identity":"0174435f-ac7d-4b0d-b270-c09bd22e28f5","added_by":"auto","created_at":"2026-01-19 08:38:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2150810,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8202041/v1/ef487247-c698-4cbe-b4e2-6e020fc38a56.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dentoalveolar Markers in A Group of Turkish Population with Different Anteroposterior Relationships: A CBCT Retrospective Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe positional relationship between maxillary and mandibular incisors and their surrounding alveolar bone is a fundamental consideration in orthodontic diagnosis and treatment planning. In individuals with varying skeletal patterns especially those with severe discrepancies or requiring orthodontic-surgical intervention orthodontic tooth movement is inherently restricted by alveolar bone morphology and periodontal condition (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe collum angle, defined as the angle between the long axis of the crown and that of the root, plays a key role in determining the degree of lingual root torque in anterior teeth. Significant increases or decreases in the collum angle may predispose roots to contact with labial or lingual cortical plates, altering force distribution and increasing mechanical stress on supporting structures during functional loading (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVariations in collum angle and alveolar bone thickness have substantial implications for orthodontic treatment planning, underscoring the importance of pre-treatment CBCT imaging to reduce the risk of complications such as dehiscence and fenestration (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Clinically, torque control is essential as it influences tooth inclination, occlusal force distribution, and the long-term stability of orthodontic outcomes. Lee et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) reported that differences in labial crown morphology and collum angle significantly affect torque expression and root positioning in maxillary anterior teeth, emphasizing the need for individualized CBCT assessment. Furthermore, clinical considerations of torque are critical for ensuring proper tooth inclination, occlusal load distribution, and long-term orthodontic stability (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, crown\u0026ndash;root angulation and buccolingual alveolar bone thickness represent critical anatomical boundaries that must be respected during safe tooth movement. Failure to consider such factors may result in iatrogenic complications, including external apical root resorption, bone dehiscence, fenestration, gingival recession, and attachment loss (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Thin buccolingual alveolar bone or pronounced collum angles increase the risk of cortical plate perforation and reduced periodontal support. Prior CBCT studies have documented the development or progression of dehiscence during orthodontic treatment, particularly on the buccal surfaces of anterior teeth (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Root displacement beyond the alveolar housing may lead to root exposure, mobility, soft tissue recession, and, in severe cases, tooth loss (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Therefore, inadequate attention to collum angle and alveolar bone thickness during tooth movement may compromise root integrity and periodontal health, increasing relapse risk and jeopardizing long-term stability (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious research has indicated that both the collum angle and buccolingual alveolar bone thickness vary depending on anteroposterior dental and skeletal patterns. Issrani et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) demonstrated that individuals with Class II Division 2 malocclusion exhibit a significantly higher collum angle compared with other groups, although no significant differences were observed between ethnic cohorts. Ma et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) reported reduced anterior alveolar bone thickness in patients with severe skeletal Class III malocclusion compared with those with severe skeletal Class II malocclusion. Additionally, the enlarged collum angle commonly observed in Class II Division 2 malocclusion may complicate torque and intrusion mechanics, increasing the risk of palatal cortical plate perforation (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study aims to investigate variations in collum angle and buccopalatal/lingual alveolar bone thickness in incisors and premolars across different anteroposterior malocclusion groups, considering age, sex, and skeletal classification. Furthermore, we sought to evaluate the correlation between collum angle and alveolar bone thickness in these populations.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003e This retrospective, single-center study was conducted at the Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Bolu Abant İzzet Baysal University. The study protocol was approved by the Ethics Committee of Istanbul Medipol University and adhered to the ethical principles outlined in the Declaration of Helsinki. Ethical approval was granted on January 7, 2025, under approval number E-10840098-202.3.02-974. The need for informed consent was waived due to the use of archived CBCT images collected for diagnostic purposes.\u003c/p\u003e \u003cp\u003e This observational study was designed and reported in accordance with the STROBE reporting guidelines (von Elm et al., PLoS Med 2007). CBCT imaging was justified according to the radiation exposure recommendations of the Hargreaves and American Association of Endodontists (AAE) guidelines to ensure appropriate indication.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study Population\u003c/h2\u003e \u003cp\u003eCBCT records were retrospectively retrieved from the digital archives of the Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Bolu Abant Izzet Baysal University, covering the period from January 2020 to October 2024. A total of 200 Turkish patients (age range: 12\u0026ndash;37 years) with different anteroposterior skeletal relationships were included in this study. Demographic and clinical characteristics of the participants are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eCharacteristics of patients participating in the study\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ef\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65\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=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;= 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2div1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2div2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\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=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample Size Calculation\u003c/h2\u003e \u003cp\u003eThe sample size was calculated a priori using G*Power (version 3.1.9.7), based on assumptions drawn from previous literature to ensure adequate statistical power. A power of 0.80 (80%), type I error (α) of 0.05, and type II error (β) of 0.20 were selected (Schober \u0026amp; Vetter, 2019; Kang, 2021; Riesthuis, 2024). A minimum of 128 subjects was required to detect a medium effect size (Cohen\u0026rsquo;s d\u0026thinsp;=\u0026thinsp;0.5) for independent samples t-tests. Additionally, for one-way ANOVA involving four groups the maximum number of malocclusion classes assessed a minimum of 180 subjects was needed to detect a medium effect size (f\u0026thinsp;=\u0026thinsp;0.25). Accordingly, a total sample size of 200 participants was deemed sufficient.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Inclusion/ Exclusion Criteria\u003c/h2\u003e \u003cp\u003eThe study included patients aged between 12 and 37 years who underwent CBCT imaging as part of orthodontic treatment planning. Inclusion criteria were as follows: presence of fully erupted incisors and premolars in both arches, no history of orthodontic treatment, and availability of high-resolution CBCT images suitable for precise measurement of the collum angle and alveolar bone dimensions. The patients were categorized according to the British Standards Institution (BSI) incisor classification: Class I, Class II/1, Class II/2, and Class III.\u003c/p\u003e \u003cp\u003eExclusion criteria included individuals with a history of craniofacial syndromes or anomalies, previous trauma or surgery in the anterior region, periodontal disease affecting anterior teeth, missing anterior teeth, restorations that could obscure anatomical landmarks, or poor CBCT image quality. Additionally, patients with active pathological lesions in the maxillofacial region or incomplete medical records were excluded. After applying these criteria, 20 of the initial 220 CBCT scans were excluded due to insufficient image quality, and the final sample selection is illustrated in the flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. CBCT Acquisition and Image Analysis\u003c/h2\u003e \u003cp\u003eCBCT images were acquired at the Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Bolu Abant Izzet Baysal University, using the i-CAT 3D Imaging System (Imaging Sciences International, Hatfield, PA, USA) equipped with an amorphous silicon flat-panel detector (23.8 cm \u0026times; 19.2 cm). Scans were obtained under standardized exposure settings (FOV: 16 \u0026times; 9 cm; voxel size: 0.3 mm; 5 mA; 120 kVp; 8.9 s). Patients were seated with head support, and laser guide lines were used to standardize positioning by aligning the vertical beam with the facial midline and the horizontal beam with the lip commissures.\u003c/p\u003e \u003cp\u003eAll datasets were imported into i-CAT Vision software for initial orientation, after which measurements were performed using OnDemand3D (Cybermed Inc., Seoul, South Korea) and Dolphin Imaging (Dolphin Imaging \u0026amp; Management Solutions, Chatsworth, CA, USA). Head orientation was refined in the axial, sagittal, and coronal planes to approximate natural head posture before analysis. A single calibrated dentomaxillofacial radiologist (A.A.), with 18 years of CBCT experience, performed all measurements twice with a 2-week. Intra-observer reliability was high (ICC\u0026thinsp;=\u0026thinsp;0.92; 95% CI: 0.88\u0026ndash;0.96). When the two repeated measurements differed by more than 2\u0026deg;, a third measurement was performed and the final value was calculated as the average of the three measurements.Images were evaluated under dim lighting, and window settings were adjusted when necessary to improve visualization. To assess intra-observer variation, 30 images were re-measured two weeks apart. If the difference between the two measurements exceeded 2\u0026deg;, a third measurement was performed, and the final value was recorded as the average of the three measurements.\u003c/p\u003e \u003cp\u003eThis study evaluated collum angles and buccal, lingual, and palatal alveolar bone thicknesses\u0026mdash;measured 5 mm apical to the cementoenamel junction (CEJ) across Class I, Class II Division 1, Class II Division 2, and Class III malocclusion groups.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCollum Angle (\u0026deg;)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe oblique slicing method was used to measure the collum angle. Three parasagittal planes were generated for each tooth: one through the mesiodistal midpoint, one through the buccolingual midpoint, and one at the cemento-enamel junction (CEJ). The angle was defined between the crown's extended long axis and the root's long axis.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA positive value was recorded when the crown axis extended labially relative to the root axis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eZero was recorded when both axes were aligned.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA negative value was recorded when the crown axis extended lingually.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCrown axis ran from incisal midpoint to CEJ center, while root axis extended to apex. (Fıgure 2)\u003c/p\u003e \u003cp\u003eFor premolars, the crown axis ran from the central groove to the CEJ midpoint, and the root axis from this point to the midpoint between buccal and palatal root apices (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAlveolar Bone Thickness (mm)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlveolar bone thickness was measured on sagittal CBCT sections through the mesiodistal midpoint of each tooth.\u003c/p\u003e \u003cp\u003eIncisors and Canines:\u003c/p\u003e \u003cp\u003eBuccal and palatal bone thickness were measured 5 mm apical to the CEJ as the distance from the outer cortical plate to the corresponding tooth surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMaxillary Premolars:\u003c/p\u003e \u003cp\u003eBuccal bone thickness was measured from the buccal cortical plate to the buccal root surface, and palatal bone thickness from the palatal plate to the palatal root.\u003c/p\u003e \u003cp\u003eMandibular Premolars:\u003c/p\u003e \u003cp\u003eBuccal and lingual bone thicknesses were measured using the same method as mandibular anterior teeth (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cb\u003eCollum Angle Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMandibular anterior teeth exhibited consistently positive collum angles across all malocclusion groups, reflecting a stable crown\u0026ndash;root inclination pattern. In contrast, maxillary teeth showed marked intergroup variability, particularly in the central incisors, where the angulation pattern was strongly influenced by the sagittal incisor relationship.\u003c/p\u003e \u003cp\u003eA statistically significant difference was detected in the upper left central incisor (tooth 21), where Class II Division 2 presented the greatest positive collum angle, followed by Class III, while Class II Division 1 exhibited reduced or negative values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Post-hoc comparisons confirmed that CII/2\u0026thinsp;\u0026gt;\u0026thinsp;CI and CII/1, and CIII\u0026thinsp;\u0026gt;\u0026thinsp;CI. In the mandibular dentition, significant intergroup variation occurred only in the lower left lateral incisor (tooth 32) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Class III patients had the highest positive angles, followed by Class II Division 1, whereas Class II Division 2 had the lowest values. Post-hoc tests showed CI\u0026thinsp;\u0026lt;\u0026thinsp;CII/1 and CIII, and CIII\u0026thinsp;\u0026gt;\u0026thinsp;CII/2. No other maxillary or mandibular teeth exhibited statistically meaningful differences.\u003c/p\u003e \u003cp\u003eOverall, collum angle morphology showed the greatest deviations in CII/2 and CIII groups, likely representing dentoalveolar compensations to sagittal skeletal discrepancy. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the mean collum angle values and standard deviations for each tooth across malocclusion groups.\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\u003eInterclass Comparison of Collum Angle Measurements Across Maxillary and Mandibular Teeth\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCII/1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eCII/2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eCIII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;149)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;18)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;21)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;12)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTooth group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTooth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eη\u0026sup2;p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003ePost-Hoc\u003c/p\u003e \u003cp\u003e(LSD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003cp\u003ecentral\u003c/p\u003e \u003cp\u003eincisor\u003c/p\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003cp\u003elateral\u003c/p\u003e \u003cp\u003eincisor\u003c/p\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eCI\u0026thinsp;\u0026lt;\u0026thinsp;CII/1 and CIII,\u003c/p\u003e \u003cp\u003eCIII\u0026thinsp;\u0026gt;\u0026thinsp;CII/2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003cp\u003ecanine\u003c/p\u003e \u003cp\u003eL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003cp\u003efirst\u003c/p\u003e \u003cp\u003epremolar\u003c/p\u003e \u003cp\u003eL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003cp\u003eSecond\u003c/p\u003e \u003cp\u003epremolar\u003c/p\u003e \u003cp\u003eL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003ecentral\u003c/p\u003e \u003cp\u003eincisor\u003c/p\u003e \u003cp\u003eU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eCII/2\u0026thinsp;\u0026gt;\u0026thinsp;CI and CII/1,\u003c/p\u003e \u003cp\u003eCIII\u0026thinsp;\u0026gt;\u0026thinsp;CII/1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003eLateral\u003c/p\u003e \u003cp\u003eincisor\u003c/p\u003e \u003cp\u003eU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003ecanine\u003c/p\u003e \u003cp\u003eU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003efirst\u003c/p\u003e \u003cp\u003epremolar\u003c/p\u003e \u003cp\u003eU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003esecond\u003c/p\u003e \u003cp\u003epremolar\u003c/p\u003e \u003cp\u003eU5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003cem\u003e** Significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** Significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 η\u0026sup2;p (effect size): 0.01\u0026thinsp;=\u0026thinsp;small, 0.06\u0026thinsp;=\u0026thinsp;medium, 0.14\u0026thinsp;=\u0026thinsp;large.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP-values were adjusted using the Bonferroni correction.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGender-Based Class Comparison of Collum Angles\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhen collum angle values were analyzed separately for males and females, no significant interclass differences were found in the lower central incisors, lower canines, lower first premolars, or lower second premolars for either gender (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn the lower lateral incisors, different patterns emerged by gender.\u003c/p\u003e \u003cp\u003eFor tooth 32, no significant difference was observed among female classes (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), whereas males showed a significant interclass difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Post-hoc analysis indicated that Class III males had the highest collum angles, Class II/1 males were higher than all groups except Class III, and Class II/2 males exhibited the lowest values.\u003c/p\u003e \u003cp\u003eFor tooth 42, the opposite pattern was found: female classes differed significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with Class II/1 females showing higher values than Class I and Class II/2, while Class III females demonstrated the largest angles overall. No significant difference was present in males (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eIn the maxillary teeth (central incisors, lateral incisors, canines, first and second premolars), no significant interclass differences were detected in either gender (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. P-values refer to one-way ANOVA performed separately for males and females. Effect size is reported as partial eta-squared (η\u0026sup2;p). Post-hoc comparisons were carried out using LSD.\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\u003eGender-Specific Interclass Comparison of Collum Angle Measurements\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"15\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCI (F/M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCII/1 (F/M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eCII/2 (F/M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eCIII (F/M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(F/M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(F/M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(F/M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;99/50)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;13/5)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;12/9)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cem\u003e(N\u0026thinsp;=\u0026thinsp;6/6)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTooth\u003c/p\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTooth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eη\u0026sup2;p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003ePost-Hoc\u003c/p\u003e \u003cp\u003e(LSD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003cp\u003ecentral\u003c/p\u003e \u003cp\u003eincisor\u003c/p\u003e \u003cp\u003eL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.9 /4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 /5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.14 /3.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94/ 4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.86/0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.006/0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.84 /3.04\u0026thinsp;\u0026plusmn;\u0026thinsp;5.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.38\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19/3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.93/ 4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.81/ 2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.93/0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.004/0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003cp\u003elateral\u003c/p\u003e \u003cp\u003eincisor\u003c/p\u003e \u003cp\u003eL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.11\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24 /4.86\u0026thinsp;\u0026plusmn;\u0026thinsp;4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28 /11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33 /3.22\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.01 /7.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.31/0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.028/0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e: CII/1\u0026thinsp;\u0026gt;\u0026thinsp;CI; CII/2\u0026thinsp;\u0026lt;\u0026thinsp;CII/1;\u003c/p\u003e \u003cp\u003eCII/1\u0026thinsp;\u0026lt;\u0026thinsp;CIII; CII/1\u0026thinsp;\u0026gt;\u0026thinsp;CII/2;\u003c/p\u003e \u003cp\u003eCIII\u0026thinsp;\u0026gt;\u0026thinsp;CI; CIII\u0026thinsp;\u0026gt;\u0026thinsp;CII/2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.48\u0026thinsp;\u0026plusmn;\u0026thinsp;4.43/ 5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;4.79 /10\u0026thinsp;\u0026plusmn;\u0026thinsp;6.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;4.78/ 4.67\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;6.78/5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.04/ 0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e**/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.064/0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e: CII/1\u0026thinsp;\u0026gt;\u0026thinsp;CI; CII/1\u0026thinsp;\u0026lt;\u0026thinsp;CIII;\u003c/p\u003e \u003cp\u003eCII/1\u0026thinsp;\u0026gt;\u0026thinsp;CII/2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003cp\u003ecanine\u003c/p\u003e \u003cp\u003eL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.66\u0026thinsp;\u0026plusmn;\u0026thinsp;5.57/ 6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.03/ 7\u0026thinsp;\u0026plusmn;\u0026thinsp;4/36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8/42\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4/ 7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87/ 5\u0026thinsp;\u0026plusmn;\u0026thinsp;4/29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.77/0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.009/0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;5.64/ 6.24\u0026thinsp;\u0026plusmn;\u0026thinsp;5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.92\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65/ 6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.58\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87/ 7.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.47/ 6.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.65/0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.013/ 0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003cp\u003efirst\u003c/p\u003e \u003cp\u003epremolar\u003c/p\u003e \u003cp\u003eL4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22.65\u0026thinsp;\u0026plusmn;\u0026thinsp;8.26/ 21.68\u0026thinsp;\u0026plusmn;\u0026thinsp;9.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e19.54\u0026thinsp;\u0026plusmn;\u0026thinsp;6.05/ 22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e24.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.61/ 26.22\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e19.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.42/ 17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.34/ 0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.026/ 0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.99/ 21.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e20.46\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23/ 24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e24.17\u0026thinsp;\u0026plusmn;\u0026thinsp;8.29/ 21.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e24.83\u0026thinsp;\u0026plusmn;\u0026thinsp;4.45/ 21\u0026thinsp;\u0026plusmn;\u0026thinsp;6.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.6/ 0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.015/ 0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003cp\u003eSecond\u003c/p\u003e \u003cp\u003epremolar\u003c/p\u003e \u003cp\u003eL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e14.06\u0026thinsp;\u0026plusmn;\u0026thinsp;7.96/ 15.52\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.15\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24/ 14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3/ 14.89\u0026thinsp;\u0026plusmn;\u0026thinsp;6.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14\u0026thinsp;\u0026plusmn;\u0026thinsp;7.72/ 14.17\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.87/0/93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.006/0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.6\u0026thinsp;\u0026plusmn;\u0026thinsp;77.08/16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.46\u0026thinsp;\u0026plusmn;\u0026thinsp;7.31/ 10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.58\u0026thinsp;\u0026plusmn;\u0026thinsp;9.25/ 15.78\u0026thinsp;\u0026plusmn;\u0026thinsp;7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.88/ 14.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.53/0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.017/0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003ecentral\u003c/p\u003e \u003cp\u003eincisor\u003c/p\u003e \u003cp\u003eU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;4.67/ -0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5/-0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;5.41/3.56\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63/3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.85/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.006/0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.75/ -1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;8.09/ -2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.85/2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.13/1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.23/0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.033/0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003eLateral\u003c/p\u003e \u003cp\u003eincisor\u003c/p\u003e \u003cp\u003eU2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 /2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;7.79/ 2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.06/ 0.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.71/3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.43/0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.021/0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.74/3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;6.85/1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.12/ 1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;9.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.96/6.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.73/0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.01/0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003ecanine\u003c/p\u003e \u003cp\u003eU3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.89\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54/-3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;8/1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07/-6.56\u0026thinsp;\u0026plusmn;\u0026thinsp;6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44/-3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.83/0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.007/0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6/ -2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.31\u0026thinsp;\u0026plusmn;\u0026thinsp;5.06/0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3/-1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;6.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-5.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5 /-0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.1/0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.049/ 0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003efirst\u003c/p\u003e \u003cp\u003epremolar\u003c/p\u003e \u003cp\u003eU4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.95\u0026thinsp;\u0026plusmn;\u0026thinsp;6.38/ 8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.54\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05/9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.02/9.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.83\u0026thinsp;\u0026plusmn;\u0026thinsp;10.268.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.74/0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.01/0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.03\u0026thinsp;\u0026plusmn;\u0026thinsp;6.43/ 7.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.69\u0026thinsp;\u0026plusmn;\u0026thinsp;6.41/ 0.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.79/ 9.22\u0026thinsp;\u0026plusmn;\u0026thinsp;9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.81/ 5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;5.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.5/0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.019/0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003cp\u003esecond\u003c/p\u003e \u003cp\u003epremolar\u003c/p\u003e \u003cp\u003eU5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.06/ 3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;5.18/3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.72/3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08/2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.85/0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.006/0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51/2.42\u0026thinsp;\u0026plusmn;\u0026thinsp;5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;2.85/-3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.42\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3/2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;8.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.68/6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;7.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.66/0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.013/0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"15\"\u003e\u003cem\u003e** Significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** Significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. F\u0026thinsp;=\u0026thinsp;Female, M\u0026thinsp;=\u0026thinsp;Male.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBuccal, Lingual, and Palatal Alveolar Bone Thickness\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThree-dimensional measurements of cortical bone thickness revealed class-dependent patterns that varied across jaw regions and surfaces (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuccal Thickness\u003c/p\u003e \u003cp\u003eSignificant differences were observed primarily in the maxillary lateral incisor (U2\u0026ndash;22), where Class I exhibited thicker buccal bone compared with Class II/2 and Class III (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Buccal cortical thinning in retroclined Class II/2 incisors and protrusive Class III incisors is consistent with their morphological compensation patterns.\u003c/p\u003e \u003cp\u003eLingual Thickness\u003c/p\u003e \u003cp\u003eLingual cortical bone was significantly different in the mandibular lateral incisors (32 and 42) and first premolar (34). Class II/2 consistently demonstrated greater lingual thickness, suggesting a posterior positional adaptation that may protect against dehiscence despite exaggerated collum angle morphology.\u003c/p\u003e \u003cp\u003ePalatal Thickness\u003c/p\u003e \u003cp\u003eSignificant palatal differences were found in the maxillary canine (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and second premolar (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), where Class II/2 once again exhibited the highest thickness. These results may reflect the palatally displaced root axes common in Class II/2 profiles.\u003c/p\u003e \u003cp\u003eTable 4. Buccal, Lingual, and Palatal Alveolar Bone Thickness Across Malocclusion Classes\u003cbr\u003e\u003cimg 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\" width=\"609\" height=\"718\"\u003e\u003c/p\u003e\u003cp\u003eAge-stratified analysis showed that collum angle differences between classes became evident only after adulthood. In participants\u0026thinsp;\u0026le;\u0026thinsp;18 years, no significant interclass differences were detected for any tooth. However, in the \u0026ge;\u0026thinsp;19-year group, clear distinctions emerged:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFor tooth 21, Class II/2 and Class III exhibited significantly higher collum angles than Class I and Class II/1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFor tooth 13, Class II/1 showed the highest values, whereas Class II/2 and Class III presented the lowest.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese findings indicate that class-related variations in collum angle morphology become more pronounced after growth completion.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelation Between Collum Angle and Alveolar Bone Thickness\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeveral tooth- and class-specific correlations were identified between the collum angle and cortical bone thickness. In the upper central incisors, Class II/2 patients showed a moderate negative correlation between collum angle and buccal thickness for tooth 11 (r = \u0026minus;\u0026thinsp;0.456, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while Class I exhibited a weak negative correlation between collum angle and palatal thickness for tooth 21 (r = \u0026minus;\u0026thinsp;0.204, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A similar weak negative correlation with palatal thickness was noted for tooth 22 in Class I (r = \u0026minus;\u0026thinsp;0.189, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFor the upper first premolars, buccal thickness was negatively correlated with collum angle in both Class I (r = \u0026minus;\u0026thinsp;0.211, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and Class III (r = \u0026minus;\u0026thinsp;0.739, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with a stronger effect in Class III.\u003c/p\u003e \u003cp\u003eIn the mandibular incisors, Class I showed a weak negative correlation between collum angle and buccal thickness for tooth 41 (r = \u0026minus;\u0026thinsp;0.256, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In contrast, a strong positive correlation was observed between collum angle and lingual thickness in Class III for tooth 42 (r\u0026thinsp;=\u0026thinsp;0.615, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). A weak negative correlation was also present in the lower canine 43 for Class I (r = \u0026minus;\u0026thinsp;0.241, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eOverall, increasing collum angle was generally associated with reduced buccal or palatal cortical thickness, except for the lower lateral incisor in Class III patients, where lingual thickness increased with greater angulation (Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eFigure 5. Heatmap illustrating the relationship between collum angle and buccal, palatal, and lingual cortical bone thickness across different tooth types and malocclusion classes.\u003c/p\u003e \u003cp\u003eBuccal, lingual, and palatal cortical measurements demonstrated several class- and gender-specific differences. Buccal thickness differences were more prominent in males, particularly in teeth such as L1\u0026ndash;41, L2\u0026ndash;42, U1\u0026ndash;21, U2\u0026ndash;12, U2\u0026ndash;22, and U3\u0026ndash;13, where Class II/2 generally showed lower values and Class I exhibited higher measurements, especially in the maxillary anterior region. In contrast, Class II/1 males displayed higher buccal thickness in the lower incisor region (L1, L2).\u003c/p\u003e \u003cp\u003eLingual thickness differences were most notable in the lower incisors and canines (L1, L2, L3). Class II/2 demonstrated the highest lingual measurements\u0026mdash;particularly in females\u0026mdash;while Class III consistently showed the lowest values. Although Class II/2 had higher averages in L4 and L5, these differences were not statistically significant.\u003c/p\u003e \u003cp\u003eFor palatal measurements, significant differences were tooth-specific. Class II/2 exhibited higher palatal thickness in U1\u0026ndash;21 (females) and U3\u0026ndash;23 (females). In U2\u0026ndash;22 (males), the Class I group had the highest values. Significant differences in the upper posterior region were limited; only U5\u0026ndash;15 in females showed higher palatal thickness in Class II/1 and Class II/2. No other palatal surfaces demonstrated statistically meaningful variation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStatistical analyses were performed using SPSS 27 (IBM Corp., Armonk, NY, USA). Data visualization (boxplots and heatmaps) was conducted in RStudio (version 4.5.1) using the ggplot2 and ggpubr packages. Descriptive statistics were reported as frequencies, percentages, means, and standard deviations.\u003c/p\u003e \u003cp\u003eNormality was assessed using skewness and kurtosis values, which fell within the \u0026plusmn;\u0026thinsp;2 range; therefore, parametric tests were applied. Group comparisons by gender were performed using independent samples t-tests, and effect sizes were reported as Cohen\u0026rsquo;s d. One-way ANOVA was used to compare collum angle and bone thickness measurements across age and malocclusion classes. Homogeneity of variances was evaluated using Levene\u0026rsquo;s test. When ANOVA showed significance, pairwise comparisons were conducted using the LSD post-hoc test. Pearson correlation analysis was used to examine relationships between collum angle and alveolar bone thickness. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study comprehensively evaluated collum angle morphology and alveolar bone thickness across different anteroposterior incisor relationships using CBCT. Understanding these parameters is essential, as tooth inclination and its position within the alveolar housing directly affect torque control, periodontal risk, and the biological limits of orthodontic tooth movement (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Unlike previous research, which often focused solely on anterior teeth or assessed bone and collum angle independently (\u003cspan additionalcitationids=\"CR17 CR18 CR19\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), the present investigation examined both parameters simultaneously and across a broader range of teeth, thereby providing a more complete morphological profile.\u003c/p\u003e \u003cp\u003eConsistent with earlier findings (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), mandibular teeth exhibited predominantly positive collum angles, suggesting a lingual root inclination that may support functional loading and occlusal adaptation. In contrast, maxillary teeth displayed class-dependent variations: Class II Division 2 and Class III subjects demonstrated significantly greater positive collum angles in the maxillary central incisors, indicating a tendency for the roots to approach the palatal cortical plate. This aligns with Issrani et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) and Rawaqa et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), who emphasized the increased palatal root positioning and restricted alveolar envelope in these malocclusion groups. Clinically, these anatomical features heighten the risk of cortical perforation during torque or intrusion movements, consistent with concerns raised by Handelman (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and Guo et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Alveolar bone thickness also varied according to malocclusion class. Class I individuals generally exhibited thicker buccal and palatal plates in the upper anterior region, whereas Class III subjects demonstrated reduced alveolar support, particularly buccally, supporting previous reports by Al-Masri et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and Lee et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These findings suggest that the alveolar housing in Class III individuals provides a narrower safe zone for labial or lingual movements. The increased palatal thickness in Class II subjects, especially in the maxillary canines and premolars, may represent a compensatory morphological adaptation consistent with observations of Rawaqa et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCorrelation analysis revealed mostly negative associations between collum angle and cortical thickness, particularly in the maxillary incisors of Class II Division 2 subjects\u0026mdash;indicating that increasing root inclination coincides with reduced buccal or palatal bone support. Similar patterns of vulnerability have been reported in finite element and CBCT-based studies linking excessive inclination to cortical thinning and dehiscence formation (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Conversely, the positive correlation in the mandibular lateral incisors of Class III subjects suggests an adaptive lingual reinforcement that may help maintain periodontal stability.\u003c/p\u003e \u003cp\u003eOverall, these findings highlight that variations in collum angle and alveolar bone morphology are strongly malocclusion-specific and clinically relevant. Root inclination directly influences the available alveolar envelope and may increase the risk of periodontal complications if orthodontic mechanics exceed the biological boundaries of the cortical plates (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Therefore, individualized torque planning is essential, particularly in Class II/2 and Class III cases, where anatomical constraints are more pronounced.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLIMITATIONS AND FUTURE DIRECTIONS LIMITATIONS AND FUTURE DIRECTIONS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study presents certain limitations that should be considered when interpreting the findings. The sample distribution across age, gender, and malocclusion groups was not fully balanced, which may have affected the robustness of subgroup comparisons. Additionally, the analysis relied solely on static CBCT-derived morphological parameters; dynamic changes that occur during active orthodontic tooth movement were not assessed. Malocclusion classification was based exclusively on BSI incisor relationships obtained from CBCT images, without incorporating soft-tissue features such as facial profile or lip dynamics, which may have provided a more comprehensive understanding of sagittal discrepancies.\u003c/p\u003e \u003cp\u003eAlthough all measurements were performed manually, recent advances in artificial-intelligence\u0026ndash;based automated segmentation have demonstrated increasing accuracy and consistency in CBCT morphological analysis (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Incorporating such AI-assisted tools in future studies may help reduce observer-dependent variability and improve analytical efficiency. Furthermore, although longitudinal CBCT imaging was not available for this cohort, ethically justified follow-up imaging would help determine whether collum angle or alveolar bone morphology undergo clinically meaningful changes over time, thereby enhancing the understanding of dentoalveolar adaptation.\u003c/p\u003e \u003cp\u003eFuture research should aim to include larger and more balanced samples, integrate soft-tissue assessments, and combine CBCT-derived morphology with biomechanical approaches such as finite element modeling. Such comprehensive analyses would deepen insights into stress distribution patterns, cortical boundary limitations, and individualized orthodontic treatment planning.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCLINICAL IMPLICATIONS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe findings underscore the need for individualized torque and movement planning, particularly for Class II Division 2 and Class III patients, who demonstrated high collum angles and reduced alveolar support. These anatomical constraints increase susceptibility to dehiscence, fenestration, and periodontal compromise during excessive torque or intrusion movements. CBCT-based assessment should therefore be considered in cases with suspected limited alveolar housing to prevent iatrogenic sequelae and to optimize orthodontic biomechanics.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMandibular teeth generally exhibited positive collum angles, while maxillary teeth demonstrated class-dependent root inclinations, with the greatest values observed in Class II Division 2 and Class III individuals. Alveolar bone thickness also varied significantly across classes, with Class III subjects showing reduced buccal support and Class II subjects demonstrating increased palatal thickness in several tooth groups. Significant correlations between collum angle and cortical thickness highlight the biomechanical relevance of root inclination in determining the limits of safe tooth movement. These findings emphasize the importance of integrating detailed CBCT-based morphology into orthodontic diagnosis and treatment planning to ensure biologically compatible tooth movement and to minimize periodontal risks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e This study was approved by the Istanbul Medipol University Research Ethics Committee (Approval No: E-10840098-202.3.02-974).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare 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\u003eData Availability:\u003c/strong\u003e The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eK.O; developed the research concept.A.A; performed the CBCT image acquisition and morphometric measurements.K.A , A.G; contributed to the study by collecting the data and compilling the references.A.A, K.O, K.A, A.G ; contributed to the writing of the manuscript.K.A; conducted the statistical analyses for the study.A.G; designed the tables and figures of the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEraydın F, Germec-Cakan D, Tozlu M, Ozdemir FI. 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Appl Sci. 2024;14(14):6298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/app14146298\u003c/span\u003e\u003cspan address=\"10.3390/app14146298\" 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":false,"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":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CBCT, collum angle, alveolar bone thickness, malocclusion, orthodontics","lastPublishedDoi":"10.21203/rs.3.rs-8202041/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8202041/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eTo assess collum angle and alveolar bone thickness across different anteroposterior incisor relationships using cone-beam computed tomography (CBCT), and to analyze the correlations between these morphological parameters.\u003c/p\u003e\u003ch2\u003eMaterials and Methods:\u003c/h2\u003e \u003cp\u003eThis retrospective CBCT study included 200 Turkish patients (12\u0026ndash;37 years) categorized according to the British Standards Institution (BSI) incisor classification: Class I, Class II/1, Class II/2, and Class III. Collum angle and buccal, palatal, and lingual alveolar bone thicknesses were measured on maxillary and mandibular anterior teeth and premolars at 5 mm apical to the CEJ using standardized sagittal slices.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eCollum angle varied significantly among malocclusion groups, with the greatest values observed in Class II/2 and Class III individuals. Mandibular teeth consistently exhibited positive collum angles, whereas maxillary teeth showed class-dependent variation. Buccal bone thickness was substantially reduced in Class III mandibular incisors and canines, while Class II/2 subjects presented increased palatal thickness in selected maxillary teeth. Correlation analysis revealed tooth- and class-specific associations, including a moderate negative correlation between collum angle and buccal thickness in Class II/2 maxillary central incisors, and a strong positive correlation with lingual thickness in Class III mandibular lateral incisors.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eCollum angle and alveolar bone thickness exhibit distinct, class-dependent morphological patterns that may restrict the biomechanical limits of orthodontic tooth movement. The pronounced root inclinations and reduced alveolar support in Class II/2 and Class III patients underscore the need for detailed CBCT evaluation to prevent periodontal complications during treatment.\u003c/p\u003e","manuscriptTitle":"Dentoalveolar Markers in A Group of Turkish Population with Different Anteroposterior Relationships: A CBCT Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 15:06:59","doi":"10.21203/rs.3.rs-8202041/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"60310035312269652501193905796413761106","date":"2026-01-25T09:44:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-14T10:40:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-22T14:25:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-27T04:58:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-27T04:55:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2025-11-25T10:21:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8408e8f8-292f-4cb0-bef9-b67f9f3b705e","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-16T15:07:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 15:06:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8202041","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8202041","identity":"rs-8202041","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}