Accuracy of an Unattended Home CPAP Titration in the Treatment of Obstructive Sleep Apnea
Bibliographic record
Abstract
Treatment of sleep apnea-hypopnea syndrome (SAHS) by fixed continuous positive airway pressure (CPAP) requires an in-laboratory titration procedure to determine the effective pressure level (Peff ). We recently reported that one auto-CPAP machine can be used without titration study allowing Peff determination. The aim of this study was to evaluate the accuracy of an auto CPAP trial at home. A 1- or 2-wk automatic CPAP trial was done at home in 40 patients by estimating the reference pressure (Pref ) to be set and a Pref + 3 cm H(2)O/-4 cm H(2)O pressure interval. Peff was then determined according to the percentage of CPAP time that was spent </= Pref. This Peff value was set on a fixed CPAP machine for two additional weeks and a control sleep study was done. The pressure setting on fixed CPAP had to be increased by 1 +/- 1 cm H(2)O (mean +/- SD) above estimated Pref. Sleep improved with fixed CPAP, with a normalization of the apnea + hypopnea index (AHI) in 38 of 40 and resumption of diurnal hypersomnolence. CPAP compliance remained excellent (CPAP use: 6.1 +/- 1.7 h/ night) after 6.5 +/- 2.8 mo of CPAP treatment. These results indicate that auto-CPAP therapy represents a new useful and accurate way to identify conventional CPAP setting outside hospital and sleep laboratories.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".