Control of Tongue Position in Patients with Obstructive Sleep Apnea: Concept and Protocol for a Randomized Controlled Crossover Trial
Bibliographic record
Abstract
We hypothesize that the control of tongue position using a newly developed tongue position retainer, where the tongue is held in a protruded position (i.e., intervention A) or in its resting position (i.e., intervention B), is effective for maintaining upper airway patency in obstructive sleep apnea (OSA) compared with no control of tongue position. This is a randomized, controlled, non-blinded, crossover, and two-armed trial (i.e., sequence AB/BA) in 26 male participants (i.e., sample size) who are scheduled to undergo a dental operation under intravenous sedation with OSA (10 ≤ respiratory event index < 30/h). Participants will be randomly allocated into either sequence by a permuted block method, stratified by body mass index. Under intravenous sedation, participants will undergo two interventions, separated by a washout period after receiving intervention A or intervention B using a tongue position retainer after baseline evaluation, before each intervention is provided. The primary outcome is the abnormal breathing index of apnea as determined by the frequency of apnea per hour. We expect that, compared with no control of tongue position, both intervention A and intervention B will improve the abnormal breathing events with superior effects achieved by the former, offering a therapeutic option for OSA.
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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 | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".