Development and validation of risk-predicting model for oral frailty in older patients with chronic pulpitis

Development and validation of risk-predicting model for oral frailty in older patients with chronic pulpitis | 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 Development and validation of risk-predicting model for oral frailty in older patients with chronic pulpitis Hui Wang, Xin Yu Ji, Yan Kong, Hai Ou Yan, Jian Chen, Juan Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7990020/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Apr, 2026 Read the published version in BMC Oral Health → Version 1 posted 13 You are reading this latest preprint version Abstract Objective This study aimed to analyze the influencing factors of oral frailty in older adults with chronic pulpitis, and to construct and validate a predictive model for oral frailty. Methods From June to August 2025, 300 older adults with chronic pulpitis were selected from Nantong Stomatological Hospital using convenient sampling. They were randomly divided into a model training set (n=210) and a validation set (n=90) at a ratio of 7:3. Data were collected using a general information questionnaire, the Oral Frailty Index-8 (OHI-8), the Dental Anxiety Scale (DAS), the Fried Frailty Phenotype (FP), the Numerical Rating Scale (NRS), and the Oral Health Literacy Scale. Logistic regression was used to identify the influencing factors of oral frailty. R software was applied to construct an oral frailty risk prediction model and draw a nomogram. Bootstrap method was used for internal validation, and the predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Hosmer-Lemeshow test. Findings The incidence of oral frailty in older adults with chronic pulpitis was 57.0%. Age (OR=2.368, P<0.001), pain intensity (OR=1.733, P=0.013), number of natural teeth (OR=1.918, P=0.006), course of chronic pulpitis (OR=3.008, P<0.001), frailty (OR=0.475, P=0.036), Dental Anxiety Scale score (OR=1.200, P<0.001), and oral health literacy (OR=0.959, P=0.006) were independent predictive factors for oral frailty. For the training set, the AUC was 0.836 (95%CI: 0.782-0.866) with a cut-off value of 0.531. The accuracy, sensitivity, and specificity were 78.1%, 79.3%, and 76.4%, respectively. The Hosmer-Lemeshow goodness-of-fit test (χ²=4.198, P =0.521) indicated good model fit. Conclusion The constructed oral frailty risk prediction model exhibits good discrimination, calibration, and clinical utility. It can provide a reference for the prevention and early screening of oral frailty in older adults with chronic pulpitis. Clinical medical and nursing staff can develop targeted nursing strategies based on the model's prediction results, strengthen comprehensive interventions, promote oral health, and prevent the progression of oral frailty. Older Adults Chronic Pulpitis Oral Frailty Prediction Model Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Oral health is a critical component of overall well-being in older adults, playing a vital role in fundamental daily functions such as chewing, swallowing, and social interaction [ 1 ] . The World Health Organization (WHO) has identified oral health as an important public health priority [ 2 ] . Oral frailty (OF), a relatively novel concept introduced by Chiang et al. in 2013, refers to a progressive decline in oral status—including tooth loss, oral hygiene, and oral function—accompanied by reduced interest in oral health, diminished physical and cognitive reserves, and eating difficulties, ultimately leading to deterioration in both physical and mental function [ 3 ] . Chronic pulpitis, a common condition in dental practice, is a chronic inflammatory disease of the dental pulp caused by bacterial infection. Its clinical manifestations include intermittent dull or timing-related pain, discomfort during biting, and pain triggered by thermal stimuli [ 4 ] . Research indicates that pain and loss of tooth structural integrity may lead patients to avoid oral hygiene practices, resulting in plaque accumulation, worsened gingival inflammation, and potential periapical pathologies, thereby increasing susceptibility to oral frailty [ 5 ] . Studies have shown that older adults with oral frailty have 2.4, 2.2, 2.3and 2.2 times higher risks of physical frailty, sarcopenia, disability, and mortality, respectively, compared to those with good oral health [ 6 ] . Importantly, oral frailty represents an intermediate state between normal and impaired oral function [ 7 ] , and is reversible in its early stages [ 8 ] . Early identification of oral frailty in older patients with chronic pulpitis, along with targeted interventions, can help reverse adverse outcomes, improve quality of life, and reduce the economic burden of medical and long-term care. A nomogram is a powerful visual analytical tool that not only illustrates trends and patterns in time-series data but also supports the prediction of future values and changes, thereby offering valuable insights for decision-making [ 9 ] . In medical contexts, nomograms allow clinicians to intuitively interpret the predictive contributions of various variables, facilitating the estimation of individual patients’ risks for specific adverse events. This study aims to develop and validate a nomogram for predicting oral frailty in older adults with chronic pulpitis, with the goal of enhancing clinical assessment and intervention strategies. Materials and methods Setting and participants This study enrolled patients with chronic pulpitis who were treated in the Department of Endodontics at Nantong Stomatological Hospital between July and October 2025. The inclusion criteria were as follows: (1) age ≥ 60 years; (2) diagnosis consistent with chronic pulpitis; (3) ability to communicate verbally without impairment; (4) voluntary participation in the study. Exclusion criteria included: (1) individuals with communication barriers; (2) those diagnosed with dementia or severe psychiatric disorders; (3) patients to cooperate fully with the study procedures. Based on the principle that the sample size should be 5 to 10 times the number of independent variables, and considering 22 potential predictors along with an anticipated 20% non-response rate, a minimum of 132 to 264 participants was required. A total of 300 patients were ultimately included. Using R software, the participants were randomly divided into a training set (n = 210) and a validation set (n = 90) at a ratio of 7:3. The study protocol was approved by the Ethics Committee of Nantong Stomatological Hospital (Approval No. PJ2025-043-01). Variables and instruments General situation questionnaire Based on a comprehensive review of the literature, a general information questionnaire was developed for this study, comprising the following sections: (1) Demographic characteristics: gender, age, residence, living alone status, educational level, personal income, and medical payment method; (2) Disease-related and clinical profiles: pain intensity, course of chronic pulpitis, number of natural teeth, toothbrushing frequency, dental floss usage frequency, history of undergoing root canal therapy, and number of chronic diseases. Oral Frailty Index-8 The Oral Frailty Index-8 (OFI-8) was developed by Tanaka et al. [ 10 ] .This questionnaire comprises 8 items across five distinct dimensions: denture use (1 item), swallowing capacity (1 item), masticatory function (3 items), oral health-related behaviors (2 items), and social participation (1 item). The total score ranges from 0 to 11, with a score of ≥ 4 indicating the presence of oral frailty. The Chinese version of the OFI-8 exhibits good reliability and validity [ 11 ] . The Cronbach's α coefficient for the scale was 0.871. Dental Anxiety Scale The Modified Dental Anxiety Scale (MDAS), developed by Humphris et al. [ 12 ] , was used to assess dental anxiety. This scale consists of 5 items, which can be categorized into two aspects: anticipatory anxiety and treatment-related anxiety. Each item is rated on a 5-point Likert scale, yielding a total score range of 5 to 25. Higher scores indicate higher levels of dental anxiety. A total score of ≥ 12 is indicative of clinically significant dental anxiety, while a score > 19 is classified as high dental anxiety. Ye et al. [ 13 ] adapted and translated this scale into Chinese, in which context it exhibited a Cronbach’s α coefficient of 0.853. The Cronbach's α coefficient for the scale was 0.879. Fried Frailty Phenotype Frailty was assessed using the Fried Frailty Phenotype, developed by Fried et al. [ 14 ] . This criteria comprises five components: unintentional weight loss, self-reported exhaustion, low physical activity, slowness (in walking speed), and weakness (in grip strength). The total score ranges from 0 to 5, with scores of < 1 indicating robustness, 1–2 indicating pre-frailty, and ≥ 3 indicating frailty. The Chinese version of Fried Frailty Phenotype exhibits good reliability and validity [ 15 ] . In this study, the Cronbach's α coefficient was 0.815. Health Literacy Dental Scale The Short Form of Health Literacy Dental Scale (HeLD-14) is used to measure the ability to seek, understand, and utilize information for making appropriate oral health decisions. This study adopted the Chinese version of the scale translated and validated by Yan Wen et al. [ 16 ] , which consists of 14 items across 7 dimensions (2 items per dimension), including Reception, Comprehension, Support, Financial Burden, Medical Consultation, Communication, and Application. Items are scored from 1 to 5, corresponding to responses ranging from "no difficulty at all" to "completely unable to do". For data analysis, scores were converted to a 0 ~ 4 scale. The total score ranges from 0 to 56, with higher scores indicating lower difficulty in performing oral health-related tasks and higher levels of oral health literacy. The scale has demonstrated good reliability and validity in the elderly population [ 17 ] , and in this study, the Cronbach’s alpha coefficient was 0.931. Data collection The research team provided standardized training for all investigators. Upon successful completion of the training, investigators used a unified script to explain the study purpose, questionnaire completion requirements, and principles of information confidentiality to participants. After obtaining informed consent, participants were guided to complete the questionnaires independently. For those unable to complete the questionnaires on their own, the investigators patiently assisted them by reading the items aloud in a neutral manner and recording their responses without prompting. All collected questionnaires were checked immediately upon retrieval. Any missing items were addressed and supplemented on the spot. Questionnaires exhibiting patterned responses, uniform answers throughout, or incomplete sections were considered invalid and excluded from analysis. Statistical analysis Statistical analyses were performed using SPSS 26.0 and R 4.2.2 software. Quantitative data that conformed to a normal distribution were expressed as mean ± standard deviation and analyzed using the independent samples t-test. Quantitative data with a non-normal distribution were presented as median [M (P25, P75)] and analyzed using the Mann–Whitney U test. Categorical data were expressed as frequencies and percentages (%) and analyzed using the \(\:{\chi\:}^{2}\) test or Fisher's exact test. Variables with statistical significance in the univariate analysis were included in the multivariate Logistic regression model. After screening the variables, a predictive model was established using the rms package in R software, and a nomogram was constructed. Model performances were verified using the Hosmer–Lemeshow goodness-of-fit test and the area under the Receiver Operating Characteristic (ROC) curve (AUC). The model’s clinical applicability was validated via DCA.A p -value < 0.05 was considered statistically significant. Results Characteristics of the participants A total of 305 questionnaires were distributed in this study. Among them, 3 incomplete questionnaires and 2 questionnaires with regular response patterns were excluded, resulting in 300 valid questionnaires recovered. The effective recovery rate of the questionnaires was 98.4%. Participants were randomly divided into a training set (n = 210) and a validation set (n = 210) at a ratio of 7:3. The incidence of oral frailty in older adults with chronic pulpitis was 57.0%. The training set included 94 males and 116 females, with 121 cases (57.6%) diagnosed with oral frailty. The validation set included 90 participants, among whom 52 cases (57.8%) were diagnosed with oral frailty. Univariate analysis of oral frailty In the training set, univariate analyses showed that age, pain level, duration of chronic pulpitis, frailty, dental anxiety, and oral health literacy were significantly associated with the detection rate of oral frailty among elderly patients with chronic pulpitis ( P < 0.05). The results of the remaining comparisons are presented in Table 1 . Table 1 Univariate analysis of oral frailty in elderly patients with chronic pulpitis Variables Total (n = 210) Non-OF (n = 89) OF (n = 121) Statistic P Dental anxiety, Mean ± SD 14.93 ± 4.71 13.39 ± 4.12 16.06 ± 4.81 t=-4.211 < 0.001 Oral health literacy, Mean ± SD 37.75 ± 12.37 40.69 ± 11.44 35.59 ± 12.62 t = 3.008 0.003 Gender, n(%) χ²=0.055 0.814 Male 94 (44.76) 39 (43.82) 55 (45.45) Female 116 (55.24) 50 (56.18) 66 (54.55) Age, n(%) χ²=9.436 0.009 60~<70 100 (47.62) 53 (59.55) 47 (38.84) 70~<80 80 (38.10) 28 (31.46) 52 (42.98) ≥80 30 (14.29) 8 (8.99) 22 (18.18) Residence, n(%) χ²=3.445 0.063 Urban 69 (32.86) 23 (25.84) 46 (38.02) Rural 141 (67.14) 66 (74.16) 75 (61.98) Living alone, n(%) χ²=0.015 0.903 Yes 91 (43.33) 39 (43.82) 52 (42.98) No 119 (56.67) 50 (56.18) 69 (57.02) Educational Level, n(%) χ²=2.603 0.457 Primary school and below 97 (46.19) 37 (41.57) 60 (49.59) Junior high school 29 (13.81) 12 (13.48) 17 (14.05) Senior high school 23 (10.95) 9 (10.11) 14 (11.57) College degree and above 61 (29.05) 31 (34.83) 30 (24.79) Personal Income, n(%) χ²=2.547 0.467 <1000 27 (12.86) 15 (16.85) 12 (9.92) 1000 ~ 3000 52 (24.76) 22 (24.72) 30 (24.79) 3000~5000 52 (24.76) 22 (24.72) 30 (24.79) Medical Payment Method, n(%) χ²=1.852 0.604 Self-payment 19 (9.05) 10 (11.24) 9 (7.44) Rural Cooperative Medical Insurance 46 (21.90) 18 (20.22) 28 (23.14) Employee Medical Insurance 72 (34.29) 33 (37.08) 39 (32.23) Urban Resident Medical Insurance 73 (34.76) 28 (31.46) 45 (37.19) Pain intensity, n(%) χ²=7.691 0.021 Mild 69 (32.86) 38 (42.70) 31 (25.62) Moderate 78 (37.14) 31 (34.83) 47 (38.84) Severe 63 (30.00) 20 (22.47) 43 (35.54) Number of natural teeth, n(%) χ²=6.120 0.047 >20 136 (64.76) 66 (74.16) 70 (57.85) 10 ~ 20 36 (17.14) 12 (13.48) 24 (19.83) 0 ~ 10 38 (18.10) 11 (12.36) 27 (22.31) Toothbrushing Frequency, n(%) χ²=4.028 0.133 Used almost every day 176 (83.81) 79 (88.76) 97 (80.17) Used occasionally per month 22 (10.48) 8 (8.99) 14 (11.57) Never used 12 (5.71) 2 (2.25) 10 (8.26) Dental Floss Usage Frequency, n(%) χ²=0.922 0.631 ≥2 times/day 36 (17.14) 17 (19.10) 19 (15.70) 1 time/day 41 (19.52) 15 (16.85) 26 (21.49) <1 time/day 133 (63.33) 57 (64.04) 76 (62.81) Course of chronic pulpitis, n(%) χ²=28.312 < 0.001 6months 124 (59.05) 39 (43.82) 85 (70.25) Undergoing Root Canal Therapy, n(%) χ²=0.093 0.760 Yes 78 (37.14) 32 (35.96) 46 (38.02) No 132 (62.86) 57 (64.04) 75 (61.98) Number of Chronic Diseases, n(%) χ²=0.145 0.703 <3 129 (61.43) 56 (62.92) 73 (60.33) ≥3 81 (38.57) 33 (37.08) 48 (39.67) Frailty, n(%) χ²=10.849 < 0.001 Yes 133 (63.33) 45 (50.56) 88 (72.73) No 77 (36.67) 44 (49.44) 33 (27.27) Variables with statistical significance were sequentially entered into a binary logistic regression model using the enter method. The coding scheme for the independent variables is shown in Table 2 . The results showed that seven factors, including age, pain level, number of natural teeth, duration of chronic pulpitis, frailty, dental anxiety, and oral health literacy, were included in the model. See Table 3 . Table 2 Assignment methods for independent variables Variables Assignment Age 60~<70 = 1, 70~<80 = 2, ≥ 80 = 3 Pain intensity Mild = 1, Moderate = 2, Severe = 3 Number of natural teeth ≥ 20 = 1, 10 ~ = 2, 0 ~ 9 = 3 Course of chronic pulpitis 6months = 3 Frailty No = 0, Yes = 1 Dental anxiety Original value input Oral health literacy Original value input Table 3 Logistic regression analysis of oral frailty in elderly patients with chronic pulpitis Variables β S. E Z P OR (95%CI) Intercept -5.877 1.504 -3.908 < 0.001 0.003 (0.000 ~ 0.053) Age 0.862 0.259 3.329 < 0.001 2.368 (1.426 ~ 3.934) Pain intensity 0.550 0.221 2.485 0.013 1.733 (1.123 ~ 2.674) Number of natural teeth 0.651 0.239 2.725 0.006 1.918 (1.201 ~ 3.063) Course of chronic pulpitis 1.101 0.250 4.406 < 0.001 3.008 (1.843 ~ 4.909) Frailty -0.745 0.356 -2.094 0.036 0.475 (0.237 ~ 0.953) Dental anxiety 0.182 0.042 4.322 < 0.001 1.200 (1.105 ~ 1.303) Oral health literacy -0.041 0.015 -2.761 0.006 0.959 (0.932 ~ 0.988) OR: Odds Ratio, CI: Confidence Interval Construction and performance analysis of a nomogram for predicting oral frailty A nomogram for predicting the risk of oral frailty in elderly patients with chronic pulpitis was constructed based on the independent predictors variables identified by binary logistic regression, as shown in Fig. 1 .In practice, healthcare providers first locate the score for each predictor on the top axis (Points) according to the patient’s characteristics, sum these scores to obtain the total points, and then draw a vertical line downward from the total points to intersect with the bottom axis. The corresponding value at this intersection represents the patient’s probability of developing oral frailty. Internal validation was performed using the bootstrap resampling method with 1,000 repetitions. In the training set, the area under the receiver operating characteristic curve (AUC) was 0.836 (95% CI: 0.782ཞ0.890), with an optimal cut-off value of 0.531, a specificity of 0.850, and a sensitivity of 0.793. In the validation set, the AUC was 0.838 (95% CI: 0.753ཞ0.923), with an optimal cut-off value of 0.667, a specificity of 0.850, and a sensitivity of 0.740, indicating good predictive performance (Fig. 2 ). The Hosmer–Lemeshow test yielded χ² = 4.198 ( P = 0.521) in the training set and χ² = 11.161 ( P = 0.132) in the validation set, suggesting good model fit (Fig. 3 ). A decision curve analysis was performed for the oral frailty prediction model. The “All” line represents the scenario where all elderly patients are classified as positive, and the “None” line represents the scenario where no elderly patients are classified as positive; the “Model” line represents the predicted positive rate of the model. When the threshold probability was set between 8% and 94%, the decision curve of the model outperformed both the None and All lines, indicating a high net benefit and clinical predictive value (Fig. 4 ). Discussion This study developed and validated a prediction model for oral frailty in older patients with chronic pulpitis. The observed oral frailty prevalence in our cohort was 57.0%, substantially higher than rates reported in the general older population [ 18 ] . This discrepancy likely stems from differences in the study population characteristics and criteria. Previous research has established oral frailty as a significant risk factor for adverse health outcomes in older adults with chronic conditions [ 19 ] . Consequently, establishing an effective risk prediction model for early screening and targeted clinical intervention in this population is of considerable importance. Our model was constructed based on a systematic literature review and consolidated clinical expertise, ensuring a sound scientific basis and clinical relevance. Through binary logistic regression analysis, we identified seven predictors with significant prognostic value to build a nomogram. The model exhibited robust performance upon internal validation using the bootstrap method (1000 repetitions), with area under the curve values of 0.836 (training set) and 0.838 (validation set), exceeding the acceptable threshold of 0.70. Calibration curves showed strong agreement between predictions and observations. The model achieved an overall accuracy of 78.1%, with a sensitivity of 79.3% and a specificity of 76.4%, confirming excellent calibration performance and reliability. Furthermore, decision curve analysis confirmed the model's clinical utility, as it provided a net benefit across a wide range of threshold probabilities against the "treat all" or "treat none" strategies. The included predictors are readily assessable through routine patient interviews, enhancing the model's practicality for clinical implementation. The results of this study identified age, natural teeth, duration of chronic pulpitis, and pain intensity as significant risk factors for oral frailty in older patients with chronic pulpitis. The positive association between advanced age and increased risk of oral frailty is consistent with previous findings [ 20 ] . The aging process entails physiological declines, including reduced salivary secretion, degenerative gingival changes, and root exposure [ 21 ] . These alterations not only elevate susceptibility to caries and periodontal disease but also weaken the oral cavity's defense against infection and mechanical stress. Additionally, age-related enamel wear, dentin hypersensitivity, tooth loss, and impaired oral immunity collectively diminish masticatory efficiency and the oral environment's self-cleaning capacity [ 22 ] . A reduced number of natural teeth was another strong predictor, aligning with findings by Luo et al. [ 23 ] . Tooth loss directly compromises mastication and speech. Crucially, it often leads to the avoidance of fibrous foods like meats and vegetables, resulting in nutritional imbalances and disuse atrophy of the oral musculature. Consequently, as Julkunen et al. emphasize, preserving natural dentition is a key protective measure against oral frailty [ 24 ] . Furthermore, a longer duration of chronic pulpitis was found to be an independent predictor of oral frailty. As a persistent source of oral infection, it maintains a state of local inflammation, pain, and functional impairment. This condition progressively undermines masticatory efficiency and oral hygiene, restricts nutrient intake, disrupts the oral microbiome, and may even exacerbate systemic inflammation via inflammatory mediators [ 25 ] , thereby accelerating the onset and progression of oral frailty. Similarly, pain intensity was significantly correlated with oral frailty risk. Persistent pain can induce dental anxiety and avoidance behaviors, causing patients to neglect oral hygiene and delay treatment. Furthermore, to minimize discomfort, patients often adopt a soft, low-nutrient diet, avoiding foods that require vigorous chewing. This can lead to mucosal regenerative impairment, compromised local immunity, and increased infection risk. Concurrently, inadequate functional stimulation promotes disuse atrophy of masticatory muscles, creating a vicious cycle that further reduces chewing efficiency and elevates frailty risk [ 18 ] . The results of this study indicated that frailty is a risk factor for oral frailty, which is consistent with the findings reported by Kobayashi et al. [ 26 ] . This association can be explained through several interconnected pathways. First, the generalized sarcopenia characteristic of frailty impairs swallowing-related muscles, leading to decreased masticatory efficiency and swallowing coordination disorders [ 27 ] . Second, the physical exhaustion and limited mobility associated with frailty often reduce social engagement [ 28 ] . Diminished verbal communication subsequently decreases functional activation of the orofacial muscles, potentially causing secondary declines in tongue pressure and promoting pharyngeal muscle atrophy, which further accelerates oral functional decline. Furthermore, frail patients often struggle to maintain adequate oral hygiene or access dental care due to physical limitations, thereby increasing their susceptibility to oral diseases and creating a vicious cycle that promotes oral frailty [ 29 ] . Therefore, Healthcare providers should implement comprehensive strategies, including personalized exercise, nutritional support, and psychological care, to mitigate the progression of frailty. Concurrently, encouraging social interaction can help maintain orofacial muscle function and tongue mobility. This integrated approach, targeting both systemic and oral dimensions, represents a promising strategy for delaying the onset and progression of oral frailty. The present study also identified dental anxiety as a significant predictor of oral frailty. This finding aligns with that of Yi et al. [ 30 ] , who reported a positive correlation between anxiety and oral frailty scores among elderly diabetic patients. As a well-documented barrier to healthcare-seeking and treatment adherence, dental anxiety can lead to delayed management of pulpitis [ 31 ] . Such delays allow persistent inflammation to extend from the pulp cavity to periapical and periodontal tissues, undermining periodontal ligament stability, promoting tooth loosening and alveolar bone resorption, and ultimately compromising the occlusal load-bearing capacity of the dentition [ 32 ] . Concurrently, chronic inflammatory pain often restricts masticatory movements, which reduces the functional use of masticatory muscles, leading to strength loss and a decline in overall oral functional reserve—thus accelerating the progression toward oral frailty. Therefore, for elderly patients with dental anxiety, it is necessary to strengthen social connections, optimize family support, and provide psychological support. For instance, patient mutual aid groups can be established to facilitate mutual support and experience sharing among patients; family members should be encouraged to actively participate in the patients’ treatment process. Furthermore, relaxation training can be provided to help patients alleviate tension; cognitive behavioral therapy can be implemented, with regular assessment of patients’ anxiety symptoms and development of individualized treatment plans based on their specific conditions. Furthermore, oral health literacy was identified as a key predictive factor for oral frailty in this study, a result consistent with the report by Xiao et al. [ 33 ] . Patients with limited health literacy often struggle to comprehend oral health information or adhere to clinical advice, resulting in insufficient self-care awareness, neglect of daily hygiene, and infrequent dental check-ups. These behaviors impede the early detection and management of oral conditions, while the absence of effective coping strategies further increases vulnerability to oral frailty. In contrast, individuals with higher health literacy are more likely to sustain regular oral care practices and seek preventive dental services proactively [ 34 ] .Importantly, evidence from a systematic review suggests that structured oral health education can markedly enhance health literacy and self-efficacy, fostering the adoption of positive, scientifically grounded self-management behaviors [ 35 ] . To address this issue, a multi-level educational approach is warranted. At the community level, efforts should be intensified to disseminate basic oral health knowledge using health bulletins and thematic lectures. At the clinical level, providers should develop individualized educational programs grounded in behavioral theory (e.g., KAP or IKAP models) and adapted to older adults’ cognitive profiles and information consumption patterns [ 36 ] . The adoption of diversified media—such as WeChat channels, oral health apps, and instructional short videos—can further improve the reach and engagement of educational initiatives. Collectively, these strategies are expected to promote healthier oral behaviors, increase participation in routine dental visits, and ultimately mitigate the risk of oral frailty in the elderly population. A nomogram is a graphical tool that uses scaled segments to display predictive outcomes, thereby enhancing the readability and interpretability of results. The actual value of each risk factor is represented by a vertical line intersecting the score axis, with the intersection point corresponding to a specific score on the horizontal axis. For instance, consider an elderly patient with chronic pulpitis: aged 75 years (35 points), moderate pain level (12 points), 4-month duration of chronic pulpitis (42 points), 21 natural teeth (0 points), no frailty (0 points), oral health literacy score of 40 (22 points), and dental anxiety score of 12 (36 points). The total score of the nomogram model is calculated as 35་12 + 42 + 0 + 0 + 22 + 36 = 147, corresponding to a 38% risk of oral frailty, which is classified as a moderate risk. The nomogram effectively quantifies odds ratios and transforms them into an intuitive scoring system. It serves as a practical tool not only for dentists and nurses but also for non-dental professionals such as community health workers and volunteers. By providing a clear and accessible risk assessment method, it supports early intervention and personalized guidance, thereby contributing to reduced medical costs for the elderly and more efficient use of public health resources. Limitations This study has several limitations that should be considered. First, the cross-sectional design, while suitable for identifying associations between variables, precludes the establishment of causal relationships. Future longitudinal studies are warranted to dynamically monitor the development and progression of oral frailty in older adults with chronic pulpitis, which would allow for more robust causal inferences. Second, although self-reported measures offer an efficient means of capturing subjective experiences, they are susceptible to recall bias and individual interpretation, potentially influencing the accuracy of the results. To mitigate this, subsequent research should incorporate objective clinical assessments to enhance data reliability. Finally, the use of convenience sampling may limit the generalizability of our findings. Future studies should employ probability sampling strategies to improve sample representativeness and strengthen the external validity of the prediction model. Conclusion In summary, approximately 57.0% of older adults with chronic pulpitis presented with oral frailty. Factors such as age, number of natural teeth, pain intensity, duration of chronic pulpitis, frailty, dental anxiety, and oral health literacy were identified as influential in the occurrence of oral frailty. The risk prediction model for oral frailty developed in this study demonstrated reliable screening performance and could effectively predict the risk of oral frailty. The constructed nomogram is intuitive and straightforward, providing a useful tool for non-dental healthcare professionals to conduct early and preliminary screening and intervention for oral frailty in older adults with chronic pulpitis. Further validation in broader populations is warranted to confirm its generalizability and clinical utility. Abbreviations WHO World Health Organization OF Oral frailty OFI-8 Oral Frailty Index-8 MDAS Modified Dental Anxiety Scale NRS Numerical Rating Scale HeLD-14 Short Form of Health Literacy Dental Scale ROC Receiver operating characteristic curve DCA Decision curve analysis Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. The protocol was reviewed and approved by the Ethics Committee of Nantong Stomatological Hospital (Approval No. PJ2025-043-01). Written informed consent was obtained from all individual participants. Consent for publication ： Not applicable. Availability of data and materials: The data are available from the corresponding author upon reasonable request. Competing Interests ： The authors declare no conflict of interest. Funding ： Open Research Topics of the Ministry of Education Engineering Research Center for Intelligent Health Care Technology (No. JYBJNKY-2024-06). Authors' contributions ： HW: Study concept and design, data acquisition, data analysis and interpretation, writing of the original draft. XYJ: Data acquisition. YK and HOY: Study concept and design, critical revision of the manuscript. JC AND JC: Resources, critical revision of the manuscript, supervision, project administration. All the authors read and approved the final version of the manuscript. Acknowledgements ： The authors thank all the participants for their valuable contributions to this study. References Gibney JM, Naganathan V, Lim M. Oral health is Essential to the Well-Being of Older People. Am J Geriatr Psychiatry. 2021;29(10):1053–7. Benzian H, et al. WHO calls to end the global crisis of oral health. Lancet. 2022;400(10367):1909–10. Shiraishi A, Wakabayashi H, Yoshimura Y. Oral Management in Rehabilitation Medicine: Oral Frailty, Oral Sarcopenia, and Hospital-Associated Oral Problems. J Nutr Health Aging. 2020;24(10):1094–9. Feng HH. 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Oral Frailty Index-8 in the risk assessment of new-onset oral frailty and functional disability among community-dwelling older adults. Arch Gerontol Geriatr. 2021;94:104340. Tu HJ, Zhang SY, Fang YH, et al. Current situation and influencing factors of oral frailty in the community elderly[J]. Chin J Nurs. 2023;58(11):1351–6. Humphris GM, Morrison T, Lindsay SJ. The Modified Dental Anxiety Scale: validation and United Kingdom norms. Community Dent Health. 1995;12(3):143–50. Ye SR, et al. Reliability and validity evaluation of the Chinese version of the Modified Dental Anxiety Scale. J Mod Med Health. 2022;38(05):734–7. Fried LP, et al. Frailty in older adults: evidence for a phenotype. J Gerontol Biol Sci Med Sci. 2001;56(3):M146–56. Wu ZZ, et al. Comparison of the application of frailty phenotype and frailty screening scale in elderly inpatients. Chin J Nurs. 2021;56(05):673–9. Yan W, et al. Sinicization and psychometric testing of the Short-Form Oral Health Literacy Assessment Scale. Chin Nurs Res. 2021;35(20):3612–6. An R, et al. Research progress of measurement tools for oral health literacy. Chin Gen Pract Nurs. 2022;20(34):4790–6. Hoshino D, et al. Association between Oral Frailty and Dietary Variety among Community-Dwelling Older Persons: A Cross-Sectional Study. J Nutr Health Aging. 2021;25(3):361–8. Hironaka S, et al. Association between oral, social, and physical frailty in community-dwelling older adults. Arch Gerontol Geriatr. 2020;89:104105. Kugimiya Y, et al. Rate of oral frailty and oral hypofunction in rural community-dwelling older Japanese individuals. Gerodontology. 2020;37(4):342–52. Tian C, et al. Analysis of the current status and influencing factors of oral frailty in elderly patients with type 2 diabetes mellitus in Taiyuan, China. BMC Geriatr. 2025;25(1):416. Hu S, Li X. An analysis of influencing factors of oral frailty in the elderly in the community. BMC Oral Health. 2024;24(1):260. Luo W, et al. Influencing factors of oral frailty in elderly patients with type 2 diabetes in China: a cross-sectional study based on the integral model of frailty. BMC Oral Health. 2025;25(1):546. Julkunen L, et al. Oral frailty among dentate and edentate older adults in long-term care. BMC Geriatr. 2024;24(1):48. Nishimoto M et al. Severe Periodontitis Increases the Risk of Oral Frailty: A Six-Year Follow-Up Study from Kashiwa Cohort Study. Geriatr (Basel), 2023. 8(1). Kobayashi Y et al. Association of Oral Frailty with Physical Frailty and Malnutrition in Patients on Peritoneal Dialysis. Nutrients, 2025. 17(12). Cruz-Moreira K, et al. Prevalence of frailty and its association with oral hypofunction in older adults: a gender perspective. BMC Oral Health. 2023;23(1):140. Komatsu R et al. Association between Physical Frailty Subdomains and Oral Frailty in Community-Dwelling Older Adults. Int J Environ Res Public Health, 2021. 18(6). Yin Y, et al. Epidemiology and risk factors of oral frailty among older people: an observational study from China. BMC Oral Health. 2024;24(1):368. Yi H, et al. Investigation on the current status and analysis of influencing factors of oral frailty in elderly patients with diabetes mellitus. Practical Geriatr. 2025;39(07):723–6. Ni GT, Jiang L, Xia SJ. Construction and application verification of a prediction model for dental anxiety in patients undergoing end in patients undergoing endodontic treatment. Contemp Nurse (Late Issue). 2024;31(05):148–52. Qiao MT, Zheng XY, Li C. Construction and validation of a prediction model for unhealed periapical lesions after 2-year follow-up in patients with chronic periapical periodontitis undergoing root canal therapy. Reflexology Rehabilitation Med. 2025;6(09):115–8. Xiao W et al. Development and Validation of a Nomogram for Predicting Oral Frailty Risk in Elderly Patients With Ischaemic Stroke. J Clin Nurs, 2025. Yu S, et al. Impact of oral health literacy on oral health behaviors and outcomes among the older adults: a scoping review. BMC Geriatr. 2024;24(1):858. Petropoulou P et al. Oral Health Education in Patients with Diabetes: A Systematic Review. Healthc (Basel), 2024. 12(9). Liang YJ, Chen ZM, Yu TT, et al. Study on the current situation and influencing factors of oral frailty in elderly patients with chronic obstructive pulmonary disease[J]. Chin Gen Pract Nurs. 2024;22(10):1911–5. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Apr, 2026 Read the published version in BMC Oral Health → Version 1 posted Editorial decision: Revision requested 09 Mar, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviews received at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor invited by journal 30 Dec, 2025 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 19 Nov, 2025 First submitted to journal 19 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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07:45:21","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115444,"visible":true,"origin":"","legend":"","description":"","filename":"52a8cd2cd51246f1bbede43419fb459b1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7990020/v1/858edca83099e1476dec1aad.xml"},{"id":100008517,"identity":"5b8129c8-a8cf-4547-8532-a3dd58ebed81","added_by":"auto","created_at":"2026-01-12 05:55:13","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127017,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7990020/v1/0e3c77cf204e0440d626e523.html"},{"id":100361171,"identity":"54c757a5-7331-44b7-b5db-fee1cce79b83","added_by":"auto","created_at":"2026-01-16 07:44:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":259890,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting oral frailty risk in older patients with chronic pulpitis\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7990020/v1/fa2d9540fe81a0b83eacd8ac.jpeg"},{"id":100361605,"identity":"5cb4a0be-4b34-4e49-8165-eb7efca33926","added_by":"auto","created_at":"2026-01-16 07:45:21","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132689,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve (ROC) of the predictive nomogram for the risk of oral frailty in older patients with chronic pulpitis. (A) training set. (B) validation set\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7990020/v1/af93df9552f908ed9fc7f511.jpeg"},{"id":100008509,"identity":"ae2ddf33-74ec-4b7a-904a-fa8123a43c52","added_by":"auto","created_at":"2026-01-12 05:55:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171471,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve for predicting the risk of oral frailty in in older patients with chronic pulpitis by the predictive nomogram. (A) training set. (B) validation set\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7990020/v1/9e3f0a4166b6fcadcab1b954.jpeg"},{"id":100008515,"identity":"45d025b9-dc2d-4399-a790-e8248852f591","added_by":"auto","created_at":"2026-01-12 05:55:13","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161821,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis (DCA) of the prediction nomogram. (A) training set. (B) validation set\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7990020/v1/728cafc5a7f00813312a3a3f.jpeg"},{"id":106810832,"identity":"8f574413-5cc3-4775-8f35-75f0442d343c","added_by":"auto","created_at":"2026-04-13 16:16:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1569607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7990020/v1/5dba92f5-fff8-408b-b888-a9bebce609dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of risk-predicting model for oral frailty in older patients with chronic pulpitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOral health is a critical component of overall well-being in older adults, playing a vital role in fundamental daily functions such as chewing, swallowing, and social interaction\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The World Health Organization (WHO) has identified oral health as an important public health priority\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Oral frailty (OF), a relatively novel concept introduced by Chiang et al. in 2013, refers to a progressive decline in oral status\u0026mdash;including tooth loss, oral hygiene, and oral function\u0026mdash;accompanied by reduced interest in oral health, diminished physical and cognitive reserves, and eating difficulties, ultimately leading to deterioration in both physical and mental function\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChronic pulpitis, a common condition in dental practice, is a chronic inflammatory disease of the dental pulp caused by bacterial infection. Its clinical manifestations include intermittent dull or timing-related pain, discomfort during biting, and pain triggered by thermal stimuli\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Research indicates that pain and loss of tooth structural integrity may lead patients to avoid oral hygiene practices, resulting in plaque accumulation, worsened gingival inflammation, and potential periapical pathologies, thereby increasing susceptibility to oral frailty\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that older adults with oral frailty have 2.4, 2.2, 2.3and 2.2 times higher risks of physical frailty, sarcopenia, disability, and mortality, respectively, compared to those with good oral health\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Importantly, oral frailty represents an intermediate state between normal and impaired oral function\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, and is reversible in its early stages\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Early identification of oral frailty in older patients with chronic pulpitis, along with targeted interventions, can help reverse adverse outcomes, improve quality of life, and reduce the economic burden of medical and long-term care.\u003c/p\u003e \u003cp\u003eA nomogram is a powerful visual analytical tool that not only illustrates trends and patterns in time-series data but also supports the prediction of future values and changes, thereby offering valuable insights for decision-making\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. In medical contexts, nomograms allow clinicians to intuitively interpret the predictive contributions of various variables, facilitating the estimation of individual patients\u0026rsquo; risks for specific adverse events. This study aims to develop and validate a nomogram for predicting oral frailty in older adults with chronic pulpitis, with the goal of enhancing clinical assessment and intervention strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eSetting and participants\u003c/p\u003e \u003cp\u003eThis study enrolled patients with chronic pulpitis who were treated in the Department of Endodontics at Nantong Stomatological Hospital between July and October 2025. The inclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;60 years; (2) diagnosis consistent with chronic pulpitis; (3) ability to communicate verbally without impairment; (4) voluntary participation in the study. Exclusion criteria included: (1) individuals with communication barriers; (2) those diagnosed with dementia or severe psychiatric disorders; (3) patients to cooperate fully with the study procedures. Based on the principle that the sample size should be 5 to 10 times the number of independent variables, and considering 22 potential predictors along with an anticipated 20% non-response rate, a minimum of 132 to 264 participants was required. A total of 300 patients were ultimately included. Using R software, the participants were randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;210) and a validation set (n\u0026thinsp;=\u0026thinsp;90) at a ratio of 7:3. The study protocol was approved by the Ethics Committee of Nantong Stomatological Hospital (Approval No. PJ2025-043-01).\u003c/p\u003e \u003cp\u003eVariables and instruments\u003c/p\u003e \u003cp\u003eGeneral situation questionnaire\u003c/p\u003e \u003cp\u003eBased on a comprehensive review of the literature, a general information questionnaire was developed for this study, comprising the following sections: (1) Demographic characteristics: gender, age, residence, living alone status, educational level, personal income, and medical payment method; (2) Disease-related and clinical profiles: pain intensity, course of chronic pulpitis, number of natural teeth, toothbrushing frequency, dental floss usage frequency, history of undergoing root canal therapy, and number of chronic diseases.\u003c/p\u003e \u003cp\u003eOral Frailty Index-8\u003c/p\u003e \u003cp\u003e The Oral Frailty Index-8 (OFI-8) was developed by Tanaka et al.\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.This questionnaire comprises 8 items across five distinct dimensions: denture use (1 item), swallowing capacity (1 item), masticatory function (3 items), oral health-related behaviors (2 items), and social participation (1 item). The total score ranges from 0 to 11, with a score of \u0026ge;\u0026thinsp;4 indicating the presence of oral frailty. The Chinese version of the OFI-8 exhibits good reliability and validity \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The Cronbach's α coefficient for the scale was 0.871.\u003c/p\u003e \u003cp\u003eDental Anxiety Scale\u003c/p\u003e \u003cp\u003eThe Modified Dental Anxiety Scale (MDAS), developed by Humphris et al.\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, was used to assess dental anxiety. This scale consists of 5 items, which can be categorized into two aspects: anticipatory anxiety and treatment-related anxiety. Each item is rated on a 5-point Likert scale, yielding a total score range of 5 to 25. Higher scores indicate higher levels of dental anxiety. A total score of \u0026ge;\u0026thinsp;12 is indicative of clinically significant dental anxiety, while a score\u0026thinsp;\u0026gt;\u0026thinsp;19 is classified as high dental anxiety. Ye et al.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e adapted and translated this scale into Chinese, in which context it exhibited a Cronbach\u0026rsquo;s α coefficient of 0.853. The Cronbach's α coefficient for the scale was 0.879.\u003c/p\u003e \u003cp\u003eFried Frailty Phenotype\u003c/p\u003e \u003cp\u003eFrailty was assessed using the Fried Frailty Phenotype, developed by Fried et al.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. This criteria comprises five components: unintentional weight loss, self-reported exhaustion, low physical activity, slowness (in walking speed), and weakness (in grip strength). The total score ranges from 0 to 5, with scores of \u0026lt;\u0026thinsp;1 indicating robustness, 1\u0026ndash;2 indicating pre-frailty, and \u0026ge;\u0026thinsp;3 indicating frailty. The Chinese version of Fried Frailty Phenotype exhibits good reliability and validity\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. In this study, the Cronbach's α coefficient was 0.815.\u003c/p\u003e \u003cp\u003eHealth Literacy Dental Scale\u003c/p\u003e \u003cp\u003eThe Short Form of Health Literacy Dental Scale (HeLD-14) is used to measure the ability to seek, understand, and utilize information for making appropriate oral health decisions. This study adopted the Chinese version of the scale translated and validated by Yan Wen et al.\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, which consists of 14 items across 7 dimensions (2 items per dimension), including Reception, Comprehension, Support, Financial Burden, Medical Consultation, Communication, and Application. Items are scored from 1 to 5, corresponding to responses ranging from \"no difficulty at all\" to \"completely unable to do\". For data analysis, scores were converted to a 0\u0026thinsp;~\u0026thinsp;4 scale. The total score ranges from 0 to 56, with higher scores indicating lower difficulty in performing oral health-related tasks and higher levels of oral health literacy. The scale has demonstrated good reliability and validity in the elderly population\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, and in this study, the Cronbach\u0026rsquo;s alpha coefficient was 0.931.\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eThe research team provided standardized training for all investigators. Upon successful completion of the training, investigators used a unified script to explain the study purpose, questionnaire completion requirements, and principles of information confidentiality to participants. After obtaining informed consent, participants were guided to complete the questionnaires independently. For those unable to complete the questionnaires on their own, the investigators patiently assisted them by reading the items aloud in a neutral manner and recording their responses without prompting. All collected questionnaires were checked immediately upon retrieval. Any missing items were addressed and supplemented on the spot. Questionnaires exhibiting patterned responses, uniform answers throughout, or incomplete sections were considered invalid and excluded from analysis.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SPSS 26.0 and R 4.2.2 software. Quantitative data that conformed to a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and analyzed using the independent samples t-test. Quantitative data with a non-normal distribution were presented as median [M (P25, P75)] and analyzed using the Mann\u0026ndash;Whitney U test. Categorical data were expressed as frequencies and percentages (%) and analyzed using the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\chi\\:}^{2}\\)\u003c/span\u003e\u003c/span\u003etest or Fisher's exact test. Variables with statistical significance in the univariate analysis were included in the multivariate Logistic regression model. After screening the variables, a predictive model was established using the rms package in R software, and a nomogram was constructed. Model performances were verified using the Hosmer\u0026ndash;Lemeshow goodness-of-fit test and the area under the Receiver Operating Characteristic (ROC) curve (AUC). The model\u0026rsquo;s clinical applicability was validated via DCA.A \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eCharacteristics of the participants\u003c/p\u003e \u003cp\u003eA total of 305 questionnaires were distributed in this study. Among them, 3 incomplete questionnaires and 2 questionnaires with regular response patterns were excluded, resulting in 300 valid questionnaires recovered. The effective recovery rate of the questionnaires was 98.4%. Participants were randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;210) and a validation set (n\u0026thinsp;=\u0026thinsp;210) at a ratio of 7:3. The incidence of oral frailty in older adults with chronic pulpitis was 57.0%. The training set included 94 males and 116 females, with 121 cases (57.6%) diagnosed with oral frailty. The validation set included 90 participants, among whom 52 cases (57.8%) were diagnosed with oral frailty.\u003c/p\u003e \u003cp\u003eUnivariate analysis of oral frailty\u003c/p\u003e \u003cp\u003eIn the training set, univariate analyses showed that age, pain level, duration of chronic pulpitis, frailty, dental anxiety, and oral health literacy were significantly associated with the detection rate of oral frailty among elderly patients with chronic pulpitis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The results of the remaining comparisons are presented 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\u003eUnivariate analysis of oral frailty in elderly patients with chronic pulpitis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;210)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-OF\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOF\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;121)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDental anxiety, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.39\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.06\u0026thinsp;\u0026plusmn;\u0026thinsp;4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et=-4.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral health literacy, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.75\u0026thinsp;\u0026plusmn;\u0026thinsp;12.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.69\u0026thinsp;\u0026plusmn;\u0026thinsp;11.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.59\u0026thinsp;\u0026plusmn;\u0026thinsp;12.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u0026thinsp;=\u0026thinsp;3.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94 (44.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (43.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (45.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (55.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (56.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (54.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=9.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60~\u0026lt;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (47.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (59.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (38.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70~\u0026lt;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (38.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (31.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (42.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (14.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (8.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (18.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=3.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (32.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (25.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (38.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (67.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (74.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (61.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (43.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (43.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (42.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (56.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (56.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (57.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Level, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=2.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (46.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (41.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (49.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (13.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (13.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (14.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (10.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (10.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (11.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege degree and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (29.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (34.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (24.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal Income, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=2.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (12.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (16.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (9.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u0026thinsp;~\u0026thinsp;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (24.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (24.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (24.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000~\u0026lt;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (37.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (33.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (40.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (24.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (24.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (24.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical Payment Method, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=1.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-payment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (9.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (11.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (7.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural Cooperative\u003c/p\u003e \u003cp\u003eMedical Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (21.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (20.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (23.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee Medical Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (34.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (37.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (32.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban Resident Medical Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (34.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (31.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (37.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain intensity, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=7.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (32.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (42.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (25.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (37.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (34.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (38.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (22.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (35.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of natural teeth, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=6.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136 (64.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (74.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (57.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026thinsp;~\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (17.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (13.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (19.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026thinsp;~\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (18.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (12.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (22.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToothbrushing Frequency, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=4.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsed almost every day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (83.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79 (88.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (80.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsed occasionally per month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (10.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (8.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (11.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (5.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (8.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDental Floss Usage Frequency, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;2 times/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (17.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (19.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (15.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (19.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (16.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (21.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;1 time/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (63.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (64.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (62.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCourse of chronic pulpitis, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=28.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;3months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (16.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (31.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026thinsp;~\u0026thinsp;6months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (24.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (24.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (24.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;6months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (59.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (43.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (70.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndergoing Root Canal Therapy, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (37.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (35.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (38.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132 (62.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (64.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (61.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Chronic Diseases, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (61.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (62.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (60.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (38.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (37.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (39.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2;=10.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (63.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (50.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (72.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (36.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (49.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (27.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eVariables with statistical significance were sequentially entered into a binary logistic regression model using the enter method. The coding scheme for the independent variables is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results showed that seven factors, including age, pain level, number of natural teeth, duration of chronic pulpitis, frailty, dental anxiety, and oral health literacy, were included in the model. See Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eAssignment methods for independent variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssignment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60~\u0026lt;70\u0026thinsp;=\u0026thinsp;1, 70~\u0026lt;80\u0026thinsp;=\u0026thinsp;2, \u0026ge;\u0026thinsp;80\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild\u0026thinsp;=\u0026thinsp;1, Moderate\u0026thinsp;=\u0026thinsp;2, Severe\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of natural teeth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u0026thinsp;=\u0026thinsp;1, 10\u0026thinsp;~\u0026thinsp;=\u0026thinsp;2, 0\u0026thinsp;~\u0026thinsp;9\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCourse of chronic pulpitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;3months, 3\u0026thinsp;~\u0026thinsp;6months\u0026thinsp;=\u0026thinsp;2, \u0026gt;6months\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;0, Yes\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDental anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal value input\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral health literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOriginal value input\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\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\u003eLogistic regression analysis of oral frailty in elderly patients with chronic pulpitis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS. E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003 (0.000\u0026thinsp;~\u0026thinsp;0.053)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.368 (1.426\u0026thinsp;~\u0026thinsp;3.934)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.733 (1.123\u0026thinsp;~\u0026thinsp;2.674)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of natural teeth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.918 (1.201\u0026thinsp;~\u0026thinsp;3.063)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCourse of chronic pulpitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.008 (1.843\u0026thinsp;~\u0026thinsp;4.909)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.475 (0.237\u0026thinsp;~\u0026thinsp;0.953)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDental anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.200 (1.105\u0026thinsp;~\u0026thinsp;1.303)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral health literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.959 (0.932\u0026thinsp;~\u0026thinsp;0.988)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eOR: Odds Ratio, CI: Confidence Interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConstruction and performance analysis of a nomogram for predicting oral frailty\u003c/p\u003e \u003cp\u003eA nomogram for predicting the risk of oral frailty in elderly patients with chronic pulpitis was constructed based on the independent predictors variables identified by binary logistic regression, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.In practice, healthcare providers first locate the score for each predictor on the top axis (Points) according to the patient\u0026rsquo;s characteristics, sum these scores to obtain the total points, and then draw a vertical line downward from the total points to intersect with the bottom axis. The corresponding value at this intersection represents the patient\u0026rsquo;s probability of developing oral frailty. Internal validation was performed using the bootstrap resampling method with 1,000 repetitions. In the training set, the area under the receiver operating characteristic curve (AUC) was 0.836 (95% CI: 0.782ཞ0.890), with an optimal cut-off value of 0.531, a specificity of 0.850, and a sensitivity of 0.793. In the validation set, the AUC was 0.838 (95% CI: 0.753ཞ0.923), with an optimal cut-off value of 0.667, a specificity of 0.850, and a sensitivity of 0.740, indicating good predictive performance (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Hosmer\u0026ndash;Lemeshow test yielded χ\u0026sup2; = 4.198 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.521) in the training set and χ\u0026sup2; = 11.161 (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.132) in the validation set, suggesting good model fit (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A decision curve analysis was performed for the oral frailty prediction model. The \u0026ldquo;All\u0026rdquo; line represents the scenario where all elderly patients are classified as positive, and the \u0026ldquo;None\u0026rdquo; line represents the scenario where no elderly patients are classified as positive; the \u0026ldquo;Model\u0026rdquo; line represents the predicted positive rate of the model. When the threshold probability was set between 8% and 94%, the decision curve of the model outperformed both the None and All lines, indicating a high net benefit and clinical predictive value (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThis study developed and validated a prediction model for oral frailty in older patients with chronic pulpitis. The observed oral frailty prevalence in our cohort was 57.0%, substantially higher than rates reported in the general older population\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. This discrepancy likely stems from differences in the study population characteristics and criteria. Previous research has established oral frailty as a significant risk factor for adverse health outcomes in older adults with chronic conditions\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Consequently, establishing an effective risk prediction model for early screening and targeted clinical intervention in this population is of considerable importance. Our model was constructed based on a systematic literature review and consolidated clinical expertise, ensuring a sound scientific basis and clinical relevance. Through binary logistic regression analysis, we identified seven predictors with significant prognostic value to build a nomogram. The model exhibited robust performance upon internal validation using the bootstrap method (1000 repetitions), with area under the curve values of 0.836 (training set) and 0.838 (validation set), exceeding the acceptable threshold of 0.70. Calibration curves showed strong agreement between predictions and observations. The model achieved an overall accuracy of 78.1%, with a sensitivity of 79.3% and a specificity of 76.4%, confirming excellent calibration performance and reliability. Furthermore, decision curve analysis confirmed the model's clinical utility, as it provided a net benefit across a wide range of threshold probabilities against the \"treat all\" or \"treat none\" strategies. The included predictors are readily assessable through routine patient interviews, enhancing the model's practicality for clinical implementation.\u003c/p\u003e \u003cp\u003eThe results of this study identified age, natural teeth, duration of chronic pulpitis, and pain intensity as significant risk factors for oral frailty in older patients with chronic pulpitis. The positive association between advanced age and increased risk of oral frailty is consistent with previous findings\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. The aging process entails physiological declines, including reduced salivary secretion, degenerative gingival changes, and root exposure\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. These alterations not only elevate susceptibility to caries and periodontal disease but also weaken the oral cavity's defense against infection and mechanical stress. Additionally, age-related enamel wear, dentin hypersensitivity, tooth loss, and impaired oral immunity collectively diminish masticatory efficiency and the oral environment's self-cleaning capacity\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. A reduced number of natural teeth was another strong predictor, aligning with findings by Luo et al.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Tooth loss directly compromises mastication and speech. Crucially, it often leads to the avoidance of fibrous foods like meats and vegetables, resulting in nutritional imbalances and disuse atrophy of the oral musculature. Consequently, as Julkunen et al. emphasize, preserving natural dentition is a key protective measure against oral frailty\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Furthermore, a longer duration of chronic pulpitis was found to be an independent predictor of oral frailty. As a persistent source of oral infection, it maintains a state of local inflammation, pain, and functional impairment. This condition progressively undermines masticatory efficiency and oral hygiene, restricts nutrient intake, disrupts the oral microbiome, and may even exacerbate systemic inflammation via inflammatory mediators\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e, thereby accelerating the onset and progression of oral frailty. Similarly, pain intensity was significantly correlated with oral frailty risk. Persistent pain can induce dental anxiety and avoidance behaviors, causing patients to neglect oral hygiene and delay treatment. Furthermore, to minimize discomfort, patients often adopt a soft, low-nutrient diet, avoiding foods that require vigorous chewing. This can lead to mucosal regenerative impairment, compromised local immunity, and increased infection risk. Concurrently, inadequate functional stimulation promotes disuse atrophy of masticatory muscles, creating a vicious cycle that further reduces chewing efficiency and elevates frailty risk\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe results of this study indicated that frailty is a risk factor for oral frailty, which is consistent with the findings reported by Kobayashi et al.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. This association can be explained through several interconnected pathways. First, the generalized sarcopenia characteristic of frailty impairs swallowing-related muscles, leading to decreased masticatory efficiency and swallowing coordination disorders\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Second, the physical exhaustion and limited mobility associated with frailty often reduce social engagement\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Diminished verbal communication subsequently decreases functional activation of the orofacial muscles, potentially causing secondary declines in tongue pressure and promoting pharyngeal muscle atrophy, which further accelerates oral functional decline. Furthermore, frail patients often struggle to maintain adequate oral hygiene or access dental care due to physical limitations, thereby increasing their susceptibility to oral diseases and creating a vicious cycle that promotes oral frailty\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Therefore, Healthcare providers should implement comprehensive strategies, including personalized exercise, nutritional support, and psychological care, to mitigate the progression of frailty. Concurrently, encouraging social interaction can help maintain orofacial muscle function and tongue mobility. This integrated approach, targeting both systemic and oral dimensions, represents a promising strategy for delaying the onset and progression of oral frailty.\u003c/p\u003e \u003cp\u003eThe present study also identified dental anxiety as a significant predictor of oral frailty. This finding aligns with that of Yi et al.\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, who reported a positive correlation between anxiety and oral frailty scores among elderly diabetic patients. As a well-documented barrier to healthcare-seeking and treatment adherence, dental anxiety can lead to delayed management of pulpitis\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Such delays allow persistent inflammation to extend from the pulp cavity to periapical and periodontal tissues, undermining periodontal ligament stability, promoting tooth loosening and alveolar bone resorption, and ultimately compromising the occlusal load-bearing capacity of the dentition\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Concurrently, chronic inflammatory pain often restricts masticatory movements, which reduces the functional use of masticatory muscles, leading to strength loss and a decline in overall oral functional reserve\u0026mdash;thus accelerating the progression toward oral frailty. Therefore, for elderly patients with dental anxiety, it is necessary to strengthen social connections, optimize family support, and provide psychological support. For instance, patient mutual aid groups can be established to facilitate mutual support and experience sharing among patients; family members should be encouraged to actively participate in the patients\u0026rsquo; treatment process. Furthermore, relaxation training can be provided to help patients alleviate tension; cognitive behavioral therapy can be implemented, with regular assessment of patients\u0026rsquo; anxiety symptoms and development of individualized treatment plans based on their specific conditions.\u003c/p\u003e \u003cp\u003eFurthermore, oral health literacy was identified as a key predictive factor for oral frailty in this study, a result consistent with the report by Xiao et al.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Patients with limited health literacy often struggle to comprehend oral health information or adhere to clinical advice, resulting in insufficient self-care awareness, neglect of daily hygiene, and infrequent dental check-ups. These behaviors impede the early detection and management of oral conditions, while the absence of effective coping strategies further increases vulnerability to oral frailty. In contrast, individuals with higher health literacy are more likely to sustain regular oral care practices and seek preventive dental services proactively\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e.Importantly, evidence from a systematic review suggests that structured oral health education can markedly enhance health literacy and self-efficacy, fostering the adoption of positive, scientifically grounded self-management behaviors\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. To address this issue, a multi-level educational approach is warranted. At the community level, efforts should be intensified to disseminate basic oral health knowledge using health bulletins and thematic lectures. At the clinical level, providers should develop individualized educational programs grounded in behavioral theory (e.g., KAP or IKAP models) and adapted to older adults\u0026rsquo; cognitive profiles and information consumption patterns\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. The adoption of diversified media\u0026mdash;such as WeChat channels, oral health apps, and instructional short videos\u0026mdash;can further improve the reach and engagement of educational initiatives. Collectively, these strategies are expected to promote healthier oral behaviors, increase participation in routine dental visits, and ultimately mitigate the risk of oral frailty in the elderly population.\u003c/p\u003e \u003cp\u003eA nomogram is a graphical tool that uses scaled segments to display predictive outcomes, thereby enhancing the readability and interpretability of results. The actual value of each risk factor is represented by a vertical line intersecting the score axis, with the intersection point corresponding to a specific score on the horizontal axis. For instance, consider an elderly patient with chronic pulpitis: aged 75 years (35 points), moderate pain level (12 points), 4-month duration of chronic pulpitis (42 points), 21 natural teeth (0 points), no frailty (0 points), oral health literacy score of 40 (22 points), and dental anxiety score of 12 (36 points). The total score of the nomogram model is calculated as 35་12\u0026thinsp;+\u0026thinsp;42\u0026thinsp;+\u0026thinsp;0\u0026thinsp;+\u0026thinsp;0\u0026thinsp;+\u0026thinsp;22\u0026thinsp;+\u0026thinsp;36\u0026thinsp;=\u0026thinsp;147, corresponding to a 38% risk of oral frailty, which is classified as a moderate risk. The nomogram effectively quantifies odds ratios and transforms them into an intuitive scoring system. It serves as a practical tool not only for dentists and nurses but also for non-dental professionals such as community health workers and volunteers. By providing a clear and accessible risk assessment method, it supports early intervention and personalized guidance, thereby contributing to reduced medical costs for the elderly and more efficient use of public health resources.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered. First, the cross-sectional design, while suitable for identifying associations between variables, precludes the establishment of causal relationships. Future longitudinal studies are warranted to dynamically monitor the development and progression of oral frailty in older adults with chronic pulpitis, which would allow for more robust causal inferences. Second, although self-reported measures offer an efficient means of capturing subjective experiences, they are susceptible to recall bias and individual interpretation, potentially influencing the accuracy of the results. To mitigate this, subsequent research should incorporate objective clinical assessments to enhance data reliability. Finally, the use of convenience sampling may limit the generalizability of our findings. Future studies should employ probability sampling strategies to improve sample representativeness and strengthen the external validity of the prediction model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, approximately 57.0% of older adults with chronic pulpitis presented with oral frailty. Factors such as age, number of natural teeth, pain intensity, duration of chronic pulpitis, frailty, dental anxiety, and oral health literacy were identified as influential in the occurrence of oral frailty. The risk prediction model for oral frailty developed in this study demonstrated reliable screening performance and could effectively predict the risk of oral frailty. The constructed nomogram is intuitive and straightforward, providing a useful tool for non-dental healthcare professionals to conduct early and preliminary screening and intervention for oral frailty in older adults with chronic pulpitis. Further validation in broader populations is warranted to confirm its generalizability and clinical utility.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv id=\"AGS1\" class=\"AbbreviationGroupSection\"\u003e \u003cdiv class=\"Heading\"\u003e\u003c/div\u003e \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral frailty\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOFI-8\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral Frailty Index-8\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMDAS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eModified Dental Anxiety Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNRS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNumerical Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHeLD-14\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShort Form of Health Literacy Dental Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eROC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDCA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. The protocol was reviewed and approved by the Ethics Committee of Nantong Stomatological Hospital (Approval No.\u0026nbsp;PJ2025-043-01).\u0026nbsp;Written informed consent was obtained from all individual participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003cstrong\u003eNot applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe data are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eOpen Research Topics of the Ministry of Education Engineering Research Center for Intelligent Health Care Technology (No. JYBJNKY-2024-06).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eHW: Study concept and design, data acquisition, data analysis and interpretation, writing of the original draft. XYJ: Data acquisition. YK and HOY: Study concept and design, critical revision of the manuscript. JC AND JC: Resources, critical revision of the manuscript, supervision, project administration. All the authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe authors thank all the participants for their valuable contributions to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGibney JM, Naganathan V, Lim M. Oral health is Essential to the Well-Being of Older People. Am J Geriatr Psychiatry. 2021;29(10):1053\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenzian H, et al. WHO calls to end the global crisis of oral health. Lancet. 2022;400(10367):1909\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShiraishi A, Wakabayashi H, Yoshimura Y. Oral Management in Rehabilitation Medicine: Oral Frailty, Oral Sarcopenia, and Hospital-Associated Oral Problems. J Nutr Health Aging. 2020;24(10):1094\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng HH. Progress in nursing care of patients with chronic pulpitis. Chin J Urban Rural Enterp Hygiene. 2025;40(01):19\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDibello V, et al. Oral frailty and its determinants in older age: a systematic review. Lancet Healthy Longev. 2021;2(8):e507\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka T, et al. Oral Frailty as a Risk Factor for Physical Frailty and Mortality in Community-Dwelling Elderly. J Gerontol Biol Sci Med Sci. 2018;73(12):1661\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka T, et al. 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Arch Gerontol Geriatr. 2021;94:104340.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTu HJ, Zhang SY, Fang YH, et al. Current situation and influencing factors of oral frailty in the community elderly[J]. Chin J Nurs. 2023;58(11):1351\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHumphris GM, Morrison T, Lindsay SJ. The Modified Dental Anxiety Scale: validation and United Kingdom norms. Community Dent Health. 1995;12(3):143\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe SR, et al. Reliability and validity evaluation of the Chinese version of the Modified Dental Anxiety Scale. J Mod Med Health. 2022;38(05):734\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFried LP, et al. Frailty in older adults: evidence for a phenotype. J Gerontol Biol Sci Med Sci. 2001;56(3):M146\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu ZZ, et al. 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Arch Gerontol Geriatr. 2020;89:104105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKugimiya Y, et al. Rate of oral frailty and oral hypofunction in rural community-dwelling older Japanese individuals. Gerodontology. 2020;37(4):342\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian C, et al. Analysis of the current status and influencing factors of oral frailty in elderly patients with type 2 diabetes mellitus in Taiyuan, China. BMC Geriatr. 2025;25(1):416.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu S, Li X. An analysis of influencing factors of oral frailty in the elderly in the community. BMC Oral Health. 2024;24(1):260.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo W, et al. Influencing factors of oral frailty in elderly patients with type 2 diabetes in China: a cross-sectional study based on the integral model of frailty. BMC Oral Health. 2025;25(1):546.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJulkunen L, et al. Oral frailty among dentate and edentate older adults in long-term care. BMC Geriatr. 2024;24(1):48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishimoto M et al. Severe Periodontitis Increases the Risk of Oral Frailty: A Six-Year Follow-Up Study from Kashiwa Cohort Study. Geriatr (Basel), 2023. 8(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi Y et al. Association of Oral Frailty with Physical Frailty and Malnutrition in Patients on Peritoneal Dialysis. Nutrients, 2025. 17(12).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruz-Moreira K, et al. Prevalence of frailty and its association with oral hypofunction in older adults: a gender perspective. BMC Oral Health. 2023;23(1):140.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKomatsu R et al. Association between Physical Frailty Subdomains and Oral Frailty in Community-Dwelling Older Adults. Int J Environ Res Public Health, 2021. 18(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin Y, et al. Epidemiology and risk factors of oral frailty among older people: an observational study from China. BMC Oral Health. 2024;24(1):368.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYi H, et al. Investigation on the current status and analysis of influencing factors of oral frailty in elderly patients with diabetes mellitus. Practical Geriatr. 2025;39(07):723\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNi GT, Jiang L, Xia SJ. Construction and application verification of a prediction model for dental anxiety in patients undergoing end in patients undergoing endodontic treatment. Contemp Nurse (Late Issue). 2024;31(05):148\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiao MT, Zheng XY, Li C. Construction and validation of a prediction model for unhealed periapical lesions after 2-year follow-up in patients with chronic periapical periodontitis undergoing root canal therapy. Reflexology Rehabilitation Med. 2025;6(09):115\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao W et al. Development and Validation of a Nomogram for Predicting Oral Frailty Risk in Elderly Patients With Ischaemic Stroke. J Clin Nurs, 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu S, et al. Impact of oral health literacy on oral health behaviors and outcomes among the older adults: a scoping review. BMC Geriatr. 2024;24(1):858.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetropoulou P et al. Oral Health Education in Patients with Diabetes: A Systematic Review. Healthc (Basel), 2024. 12(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang YJ, Chen ZM, Yu TT, et al. Study on the current situation and influencing factors of oral frailty in elderly patients with chronic obstructive pulmonary disease[J]. Chin Gen Pract Nurs. 2024;22(10):1911\u0026ndash;5.\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":true,"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":"Older Adults, Chronic Pulpitis, Oral Frailty, Prediction Model, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7990020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7990020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective\u003c/p\u003e\n\u003cp\u003eThis study aimed to analyze the influencing factors of oral frailty in older adults with chronic pulpitis, and to construct and validate a predictive model for oral frailty.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eFrom June to August 2025, 300 older adults with chronic pulpitis were selected from Nantong Stomatological Hospital using convenient sampling. They were randomly divided into a model training set (n=210) and a validation set (n=90) at a ratio of 7:3. Data were collected using a general information questionnaire, the Oral Frailty Index-8 (OHI-8), the Dental Anxiety Scale (DAS), the Fried Frailty Phenotype (FP), the Numerical Rating Scale (NRS), and the Oral Health Literacy Scale. Logistic regression was used to identify the influencing factors of oral frailty. R software was applied to construct an oral frailty risk prediction model and draw a nomogram. Bootstrap method was used for internal validation, and the predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Hosmer-Lemeshow test.\u003cstrong\u003e \u003c/strong\u003eFindings\u003c/p\u003e\n\u003cp\u003eThe incidence of oral frailty in older adults with chronic pulpitis was 57.0%. Age (OR=2.368, P\u0026lt;0.001), pain intensity (OR=1.733, P=0.013), number of natural teeth (OR=1.918, P=0.006), course of chronic pulpitis (OR=3.008, P\u0026lt;0.001), frailty (OR=0.475, P=0.036), Dental Anxiety Scale score (OR=1.200, P\u0026lt;0.001), and oral health literacy (OR=0.959, P=0.006) were independent predictive factors for oral frailty. For the training set, the AUC was 0.836 (95%CI: 0.782-0.866) with a cut-off value of 0.531. The accuracy, sensitivity, and specificity were 78.1%, 79.3%, and 76.4%, respectively. The Hosmer-Lemeshow goodness-of-fit test (χ²=4.198, \u003cem\u003eP\u003c/em\u003e=0.521) indicated good model fit.\u003c/p\u003e\n\u003cp\u003eConclusion\u003c/p\u003e\n\u003cp\u003eThe constructed oral frailty risk prediction model exhibits good discrimination, calibration, and clinical utility. It can provide a reference for the prevention and early screening of oral frailty in older adults with chronic pulpitis. Clinical medical and nursing staff can develop targeted nursing strategies based on the model's prediction results, strengthen comprehensive interventions, promote oral health, and prevent the progression of oral frailty.\u003c/p\u003e","manuscriptTitle":"Development and validation of risk-predicting model for oral frailty in older patients with chronic pulpitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 05:55:04","doi":"10.21203/rs.3.rs-7990020/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-09T10:01:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-25T13:18:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104229610928550452764561803907125309292","date":"2026-02-04T22:37:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49144553314022626470761557451834993467","date":"2026-01-12T05:25:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-08T02:46:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175162280877574743610452429799929551579","date":"2026-01-08T01:11:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322235621405255778981635525149549075445","date":"2026-01-07T23:11:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154302924624685025040210207742222342445","date":"2026-01-07T18:49:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T05:14:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-30T10:05:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T08:09:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T01:59:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2025-11-20T01:56:18+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":"e0f352c9-cf14-4a7a-a6df-4dc86c0df8bb","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:15:19+00:00","versionOfRecord":{"articleIdentity":"rs-7990020","link":"https://doi.org/10.1186/s12903-026-08254-1","journal":{"identity":"bmc-oral-health","isVorOnly":false,"title":"BMC Oral Health"},"publishedOn":"2026-04-07 15:58:06","publishedOnDateReadable":"April 7th, 2026"},"versionCreatedAt":"2026-01-12 05:55:04","video":"","vorDoi":"10.1186/s12903-026-08254-1","vorDoiUrl":"https://doi.org/10.1186/s12903-026-08254-1","workflowStages":[]},"version":"v1","identity":"rs-7990020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7990020","identity":"rs-7990020","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}