Moving beyond bruxism episode index: Discarding misuse of the number of sleep bruxism episodes as masticatory muscle pain biomarker
Bibliographic record
Abstract
The objective of the current study was to evaluate the clinical utility of bruxism episode index in predicting the level of masticatory muscle pain intensity. The study involved adults (n = 220) recruited from the Outpatient Clinic of Temporomandibular Disorders at the Department of Experimental Dentistry, Wroclaw Medical University, during the period 2017-2022. Participants underwent medical interview and dental examination, focusing on signs and symptoms of sleep bruxism. The intensity of masticatory muscle pain was gauged using the Numeric Rating Scale. Patients identified with probable sleep bruxism underwent further evaluation through video-polysomnography. Statistical analyses included the Shapiro-Wilk test, Spearman's rank correlation test, association rules, receiver operating characteristic curves, linear regression, multivariate regression and prediction accuracy analyses. The analysis of correlation and one-factor linear regression revealed no statistically significant relationships between bruxism episode index and Numeric Rating Scale (p > 0.05 for all analyses). Examination of receiver operating characteristic curves and prediction accuracy indicated a lack of predictive utility for bruxism episode index in relation to masticatory muscle pain intensity. Multivariate regression analysis demonstrated no discernible relationship between bruxism episode index and Numeric Rating Scale across all examined masticatory muscles. In conclusion, bruxism episode index and masticatory muscle pain intensity exhibit no correlation, and bruxism episode index lacks predictive value for masticatory muscle pain. Clinicians are advised to refrain from employing the frequency of masticatory muscle activity as a method for assessing the association between masticatory muscle pain and sleep bruxism.
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How this classification was reachedexpand
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 | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".