Can Patients with Obstructive Sleep Apnea Titrate Their Own Continuous Positive Airway Pressure?
Bibliographic record
Abstract
Manual continuous positive airway pressure (CPAP) titration in a sleep laboratory is costly and limits access for diagnostic studies. Many factors affect CPAP compliance, but education and support, rather than in-laboratory CPAP titration, appear to be pivotal. Self-adjustment of CPAP at home will provide equal or superior efficacy in the treatment of obstructive sleep apnea (OSA) as compared with in-laboratory titration. A randomized, single-blind, two-period crossover trial of CPAP treatment at the in-laboratory-determined optimal pressure versus at-home self-adjustment of CPAP (starting pressure based on prediction equation). Eighteen CPAP-naive patients (16 males, 50 +/- 15 years old, apnea hypopnea index 40 +/- 20) with a new diagnosis of OSA were tested. Testing was performed before and after CPAP treatment in each of two 5-week study limbs. CPAP, compliance with CPAP treatment, the Sleep Apnea Quality of Life Index, the Functional Outcomes of Sleep Questionnaire score, the Epworth sleepiness scale score, sleep architecture, sleep apnea severity, and maintenance of wakefulness tests were performed. Both modes of CPAP treatment significantly improved objective and subjective measures of OSA, but they did not differ in efficacy. Home self-titration of CPAP is as effective as in-laboratory manual titration in the management of patients with 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 | 0.001 | 0.017 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".