Oral health quality of life is associated to jaw function and depression in patients with myogenous temporomandibular dysfunction
Bibliographic record
Abstract
OBJECTIVE: To determine which factors influence and better differentiate between good and poor oral health-related quality of life (OHRQoL) in patients with myogenous TMD and which cut-off could predict a good/poor OHRQoL. METHODS: Fifty-eight women with myogenous TMD were included. Factors of interest were collected (i.e., demographic variables, depression symptoms (Symptom Checklist-90 R (RDC/TMD)), pain intensity (Visual Analog Scale), jaw function (Mandibular Functional Limitation Questionnaire), and OHRQoL (Oral Health Impact Profile-14). A multivariable regression model, logistic regression, and receiver operating curve (ROC) analyses were conducted. RESULTS: Depression symptoms (β = 0.139) and jaw function (β = 0.478) were significantly associated with OHRQoL in the multivariable model. The best model to discriminate between good/poor OHRQoL included only jaw function (AUC = 0.90), with the best cut-off of 17 points (sensitivity: 0.93; specificity: 0.79). CONCLUSION: Depression symptoms and jaw function were significantly associated with OHRQoL. The best model and cut-off to discriminate good/poor OHRQoL included only jaw function.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".