Leak Profile Inspection During Nasal Continuous Positive Airway Pressure
Bibliographic record
Abstract
BACKGROUND: Patients treated with nasal continuous positive airway pressure (nasal CPAP) for obstructive sleep apnea (OSA) often have adverse effects from and poor adherence to CPAP. OBJECTIVE: To describe abnormal CPAP leak profiles and assess inter-observer reliability in identifying leak profiles and the correlation of leak profiles with leak rate and clinical outcomes. METHODS: In a sleep-disorders clinic we prospectively studied 35 consecutive patients newly diagnosed with moderate or severe OSA, and who had undergone polysomnographic diagnosis and nasal CPAP titration. We analyzed the data recorded by their CPAP machines during their first week of CPAP. Two independent clinical sleep specialists inspected each night's leak profiles. We defined a "continuous" leak profile segment as a leak increase of ≥ 20 L/min for > 5 min. We defined a "serrated" leak profile segment as a leak that oscillated up to ≥ 20 L/min in ≤ 5 min. With a validated questionnaire, we surveyed the patients about adverse effects. RESULTS: Overall inter-observer agreement was 88% for continuous leak and 92% for serrated leaks. The kappa values were 0.76 and 0.85, respectively. Deviance (± 2 SD) between scorers was -14% to 11% for continuous leaks, and -15% to 9% for serrated leaks. The duration of manually scored profiles correlated modestly but significantly with the machine-recorded leaks. The mean ± SD adherence to CPAP was lower in the patients with the highest quartile of continuous leak (5.28 ± 2.24 h/night versus 6.66 ± 1.72 h/night). Adverse effects increased with increasing serrated leak (P = .01). CONCLUSIONS: Manually scored leak profiles in patients treated with nasal CPAP can guide clinicians with respect to short-term adherence to nasal CPAP and adverse effects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".