Validity of different tools to assess sleep bruxism: a meta‐analysis
Bibliographic record
Abstract
This systematic review and meta-analysis (MA) aimed to evaluate the diagnostic validity of questionnaires, clinical assessment and portable diagnostic devices compared to the reference standard method polysomnography (PSG) in assessing sleep bruxism (SB). Two reviewers searched electronic databases for diagnostic test accuracy studies that compared questionnaires, clinical assessment or portable diagnostic devices for SB, with the reference standard method PSG, comprising previous studies from all languages and with no restrictions regarding age, gender or time of publication. Of the 351 articles, eight met the inclusion criteria for qualitative, and seven for quantitative analysis. The methodology of selected studies was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2). The studies were divided and analysed over three groups: three studies evaluating questionnaires, two regarding the clinical assessment of tooth wear and three covering portable diagnostic devices. The MA indicated that portable diagnostic devices showed the best validity of all evaluated methods, especially as far as a four-channel EMG/ECG recording is concerned. Questionnaires and the clinical assessment can be used as screening methods to identify non-SB individuals, although it is not that good in identifying subjects with SB. The quality of evidence identified through GRADEpro, was from very low-to-moderate, due to statistical heterogeneity between studies.
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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 | 0.030 | 0.079 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".