Sleep Position Trainer versus Tennis Ball Technique in Positional Obstructive Sleep Apnea Syndrome
Bibliographic record
Abstract
STUDY OBJECTIVE: Positional therapy (PT) is an effective therapy in positional obstructive sleep apnea syndrome (POSAS) when used, but the compliance of PT is low. The objective of this study was to investigate whether a new kind of PT is effective and can improve compliance. METHODS: 29 patients were treated with the Sleep Position Trainer (SPT), 26 patients with the Tennis Ball Technique (TBT). At baseline and 1 month polysomnography, Epworth Sleepiness Scale (ESS) and the Quebec Sleep Questionnaire (QSQ) were taken. Daily compliance was objectively measured in both groups. RESULTS: Both therapies prevent supine sleep position to a median of 0% (min-max: SPT 0.0% to 67%, TBT 0.0% to 38.9%), resulting in a treatment success (AHI <5) in 68.0% of the SPT and 42.9% of the TBT patients. The ESS at baseline was <10 in both groups. Sleep quality parameters as wake after sleep onset (WASO; p = 0.001) and awakenings (p = 0.006) improved more in the SPT group. Total QSQ scores (0.4±0.2, p = 0.03) and the QSQ domains nocturnal symptoms (0.7±0.2, p = 0.01) and social interactions (0.8±0.3, p = 0.02) changed in favor of the SPT group. Effective compliance (≥4 h/night + ≥5 days/week) was 75.9% for the SPT and 42.3% for the TBT users (p = 0.01). CONCLUSION: In mild POSAS with normal EES the new SPT device and the standard TBT are equally effective in reducing respiratory indices. However, compared to the TBT, sleep quality, quality of life, and compliance improved significantly more in the SPT group.
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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) | 0.002 | 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".