Comparison of various treatments for sleep bruxism using determinants of number needed to treat and effect size.
Bibliographic record
Abstract
PURPOSE: Sleep bruxism (SB) is associated with temporomandibular pain, headaches, tooth wear, and disruption of the bed partner's sleep. The aim of this report was to compare SB treatments from various experimental studies to guide the selection of a treatment for a large sample size study. MATERIALS AND METHODS: After a literature search, randomized controlled studies of 7 pharmacologic treatments and 3 oral devices were included. The number needed to treat (NNT) was calculated from raw data from the sleep laboratory at the Hôpital du Sacré-Coeur, Montréal or from published articles when sufficient data were available. The effect size (ES) was calculated for all included studies. In the most effective treatments, the NNT ranged from 1 to 4, while a high ES was above 0.8. RESULTS: The treatments with the best NNT and ES results were the mandibular advancement device (MAD) and clonidine. The NNT (+/-95% CI) and ES were 2.2 (1.4 to 5.3) and 1.5 for the MAD, and 3.2 (1.7 to 37.3) and 0.9 for clonidine, respectively. An NNT of 3.8 (1.9 to -69.4) and an ES of 0.6 were observed with the occlusal splint, with a reduction of 42% in the SB index. NNT could not be calculated for clonazepam, although the ES was 0.9. CONCLUSION: Although the NNT and ES results seem to indicate that the MAD and clonidine are the most promising experimental treatments, both treatments were associated with side effects (ie, discomfort for the MAD; REM suppression and morning hypotension for clonidine). The occlusal splint and clonazepam seem to be acceptable short-term alternatives, although further longitudinal, large sample size randomized controlled trials in SB management are needed.
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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 | 0.150 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".