Prevalence of Temporomandibular Disorders in Dental House Officers of Pakistan & Its Association with Biopsychosocial Factors- A Retrospective Study
Bibliographic record
Abstract
Background: Temporomandibular disorders have high prevalence, particularly in dental graduates. They have a biopsychosocial model of pathogenesis, with gender, financial status, working conditions having possible associations. Fonseca Anamnestic Index is a useful tool for assessment of presence of TMD. Objective: To assess prevalence of TMDs in dental house officers of Pakistan and to evaluate the association of TMDs with biopsychosocial factors such as gender, pay status, house job in public or private sector. Study Design: cross sectional study. Settings: Department of Prosthodontics, institute of Dentistry, CMH Lahore Medical & Dental Collage, Lahore, Pakistan. Duration: One year from February 2023 to February 2024. Methods: Data was collected via online validated forms, from 550 dental house officers, working in public and private sector, in Islamabad, Lahore and Karachi. Questions included gender, age, month, and department of house job, pay status and whether house job was in public or private sector. TMD status was evaluated via Fonseca Anamnestic Index. 520 responses were collected and analysed using SPSS 23.0. Results: Prevalence of TMDs was 61.1% out of which 43 % had mild TMD, 20% had moderate TMD and 3.1% had severe TMD. Higher, albeit statistically non-significant, prevalence was found in females, unpaid house officers and those working in public sector. Conclusion: There is high prevalence of TMDs in dental house officers in major cities of Pakistan. Association of TMD with gender, pay status, and public/private sector remains inconclusive but higher prevalence of TMDs in females, unpaid, and public sector dental house officers mandates further investigation behind causative factors, and implementation of policies to ensure paid house jobs and progressive development of healthcare system, particularly in public sector.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".