Sleep position trainer vs. tennis ball technique in positional OSA
Bibliographic record
Abstract
Introduction: standard tennis ball techniques like the positional band (PB) can be as effective as CPAP in positional OSA (POSA) but compliance is low. Objectives: can compliance of positional therapy in POSA be improved with a new device, the sleep position trainer (SPT) Therapies: The SPT is a small in supine position vibrating device with position sensors placed on the ventral thorax. The PB is a belt with three inflatable airbags worn on the back preventing supine position. Methods: 55 new patients with POSA were randomized to SPT (29) or PB (26). Standard home-PSG was done at baseline and after 1-month therapy. Quebec Sleep Questionnaire (QSQ), ESS and VAS scores were taken. The SPT device was, in a non-vibrating mode, also build in the PB for measuring daily compliance Results: comparing PSG: AHItot, AHIsup, %supTST was respectively 11.4, 30.7, 27.9% for SPT and 13.2, 37.3, 31.1% for PB. After 1 month the same parameters were respectively reduced to 3.9, 0.0, 0.0 for SPT and 5.8, 0.0, 0.0 for PB. After 1 month therapy no differences in QSQ, ESS, PSG sleep parameters were observed, however perceived therapeutic effectiveness by means of VAS was 74,5 for SPT and 55,2 for PB (P 0.02). Compliance decreased with time. At 1 month SPT and PB was used respectively in 70% and 42%. Compliance expressed as use >4 hours/night for > 5 days/week was 76% for SPT, 42% for PB (P 0.01). Dropouts were 7% in SPT, 28% in PB. Conclusions: SPT and PB effectively treat POSA when used. Only the SPT does have an acceptable compliance after 1 month.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".