Oral health quality of life is associated to jaw function and depression in patients with myogenous temporomandibular dysfunction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it