The effect of continuous positive airway pressure and mandibular advancement device on sleep bruxism intensity in obstructive sleep apnea patients
Bibliographic record
Abstract
We aimed to evaluate and compare the effects of continuous positive airway pressure (CPAP) and mandibular advancement device (MAD) in reducing the intensity of sleep bruxism (SB) in patients with obstructive sleep apnea (OSA). Forty-eight adults with OSA were subjected to single-night full polysomnography (PSG) in the Sleep Laboratory of the Wroclaw Medical University. The respiratory events and bruxism episodes were scored according to the standards of the American Academy of Sleep Medicine. The patients were assigned to the CPAP treatment or the MAD treatment in accordance to apnea-hypopnea index (AHI). The second PSG examination was conducted during the MAD or CPAP treatment to assess the effect of treatment on bruxism episode index (BEI) and AHI. The mean AHI and mean BEI in the study material were estimated to be 30.05 ± 15.39 and 5.10 ± 5.31, respectively. The bruxism parameters were significantly decreased in both the CPAP and MAD groups. Compared to the MAD, the CPAP treatment was more effective in reducing AHI; however, there was no significant difference in effectiveness of CPAP and MAD treatment in BEI reduction. Both CPAP and MAD treatments were effective against SB coexisting with OSA. Due to the application of these treatment options, the risk of OSA should be estimated in patients with SB.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".