Weighing the potential effectiveness of various treatments for sleep bruxism.
Bibliographic record
Abstract
Sleep bruxism may lead to a variety of problems, but its pathophysiology has not been completely elucidated. As such, there is no definitive treatment, but certain preventive measures and/or drugs may be used in acute cases, particularly those involving pain. This article is intended to guide clinician scientists to the treatment most appropriate for future clinical studies. To determine the best current treatment, 2 measures were used to compare the results of 10 clinical studies on sleep bruxism, 3 involving oral devices and 7 involving pharmacologic therapy. The first measure, the number needed to treat (NNT), allows several randomized clinical studies to be compared and a general conclusion to be drawn. The second measure, effect size, allows evaluation of the impact of treatment relative to a placebo using different studies of similar design. Taking into account the NNT, the effect size and the power of each study, it can be concluded that the following treatments reduce sleep bruxism: mandibular advancement device, clonidine and occlusal splint. However, the first 2 of these have been linked to adverse effects. The occlusal splint is therefore the treatment of choice, as it reduces grinding noise and protects the teeth from premature wear with no reported adverse effects. The NNT could not be calculated for an alternative pharmacologic treatment, short-term clonazepam therapy, which had a large effect size and reduced the average bruxism index. However, the risk of dependency limits its use over long periods. Assessment of efficacy and safety of the most promising treatments will require studies with larger sample sizes over longer periods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".