Prevalencia de la disfunción temporomandibular en trabajadores de la industria. Asociación con el estrés y el trastorno del sueño
Bibliographic record
Abstract
Objective To determine the presence of temporomandibular disorders (TMD), sleep disorders and stress, as well as the association between these factors, among industrial workers in São Paulo State, Brazil. Material and Methods Fonseca's questionnaire was used to verify the level of TMD, the Toronto Sleep Assessment Questionnaire (SAQ) was applied to check the quality and occurrence of sleep disorders, and the Social Readjustment Rating Scale (SRRS) was used to check the degree of stress. The data collected were tabulated with Epi InfoTM 7 and statistically analyzed using the chi-square test, with a 5 % significance level. Results 104 workers participated in the survey. Most were male (74 %) between 35 and 44 years of age (26 %). Thirty-seven (35.6 %) had some degree of disorder, 65 (62.5 %) presented with sleep disorders, and 6 (5.8 %) presented with higher degrees of stress. After statistical analysis, there was no significant association between stress and temporomandibular disorders (TMD). However, there was an association between quality of sleep and sex of the individual with TMD (p<0.01). Conclusion We conclude that a high percentage of the analyzed population has sleep disorders and TMD. Sex and the quality of sleep influence the occurrence of TMD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".