Temporomandibular Disorders among Dental Students in Pakistan: Assessment of Prevalence, Severity, and Associated Factors Based on Questionnaire
Bibliographic record
Abstract
Objective. To evaluate the prevalence, severity, and associated factors of temporomandibular disorder (TMD) among dental students. Methods. This cross-sectional study was performed on undergraduate dental students from four dental colleges in Punjab, Pakistan. Fonseca’s questionnaire was used to measure the prevalence and severity of the TMD among the study participants. Bivariate and multiple logistic regression analyses were performed. Results. Of 364 dental students, 323 returned the completed questionnaires and the response rate of the study was 88.7%. The study included 52.6% males and 47.4% females. The prevalence of TMD was 66.9% with mild TMD in 40.90%, moderate TMD in 14.6%, and severe TMD in 11.50% of the participants. Psychological stress (29.6%), malocclusion (20%), and hypersensitivity (19.5%) were common among participants. The mean TMD score of the sample was 31.54 ± 24.86 which was significantly higher among participants with no/school-educated mothers ( P = 0.021 ) and fathers ( P = 0.002 ). The participants with arthritis (72.81 ± 32.19) and malocclusion (59.46 ± 31.09) and those who received orthodontic treatment (53.21 ± 34.21) demonstrated higher TMD. After controlling for other study variables, the participants with arthritis were 4.71 times more likely to have moderate/severe TMD ( P = 0.042 ) than those without arthritis. Similarly, the participants with malocclusion had significantly higher odds (OR = 3.57, P = 0.029 ) of having moderate/severe TMD than those without malocclusion. Conclusion. This sample of dental students demonstrated a high prevalence and severity of TMD. The participants with arthritis and malocclusion demonstrated higher TMD. The study findings underscore the importance of prevention, early diagnosis, and management of TMD among the dental students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".