Comorbid conditions associated with painful temporomandibular disorders in adolescents from Brazil, Canada and France: A cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Painful temporomandibular disorder (TMD) is common among adolescents. Presence of painful comorbidities may worsen painful TMD and impact treatment effectiveness. OBJECTIVE: The aim of this study was to assess the association between painful TMD and comorbidities. METHODOLOGY: In this cross-sectional study, adolescents were recruited in Montreal (Canada), Nice (France) and Arceburgo (Brazil). Reliable instruments were used to assess painful TMD and comorbidities. Multivariable logistic and linear regression analyses were conducted to assess the study aims. RESULTS: The prevalence of self-reported painful TMD was estimated at 31.6%; Arceburgo (31.6%), Montreal (23.4%) and Nice (31.8%). Painful TMD was more common among girls than boys (OR = 1.96). Painful TMD was associated with a higher number of comorbidities (OR = 1.77); Arceburgo (OR = 1.81), Montreal (OR = 1.80) and Nice (OR = 1.72). A stronger association was found between painful TMD and headaches (OR = 4.09) and a weaker one with stomach pain (OR = 1.40). Allergies were also related to painful TMD (OR = 1.43). CONCLUSION: Painful TMD was associated with comorbidities. Headaches were consistently associated with painful TMD. Other associations were modified by sex and/or covariates related to the cities where participants were recruited.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".