Efficacy and patient satisfaction with autoadjusting CPAP with variable expiratory pressure vs standard CPAP: a two-night randomized crossover trial
Bibliographic record
Abstract
Expiratory pressure relief (C-Flex) technology monitors the patient's airflow during expiration and reduces the pressure in response to the patient. Increased comfort levels associated with C-Flex therapy have potential to improve patient adherence to therapy. The purpose of this study was to assess the combination of autoadjusting CPAP (APAP) and C-Flex in terms of (1) treatment efficacy, and (2) patient preference when compared to standard CPAP. Fifteen patients who had previously undergone formal CPAP titration polysomnography were treated with either one night of the APAP with C-Flex or one night of conventional CPAP, in a crossover trial. Patient satisfaction levels were recorded using visual analog scales (VAS) on the morning after the study. Mean patient age was 50 +/- 12 years, body mass index (BMI) was 36 +/- 6 kg/m(2), baseline AHI was 53 +/- 31 events/h, and CPAP Pressure was 11 +/- 2 cm/H(2)O. APAP with C-Flex was as effective as CPAP, with no differences detected in sleep latency (17 +/- 5 vs 12.3 +/- 3 min, p = 0.4), or respiratory indices (AHI of 4.2 +/- 2 vs 2.4 +/- 0.7 events/h, p = 0.1). VAS scores (scale 0-10) indicated a trend towards increased patient satisfaction while using APAP with C-Flex (7.9 vs 7.2, p = 0.07). 10 patients expressed a preference for APAP with C-Flex (VAS, 0 to 10) over standard CPAP (total positive score of 68, mean score of 4.8 +/- 4.3). One patient expressed no preference. Four patients expressed a preference for CPAP (total positive score of 13, mean score of 0.9 +/- 1.9) (APAP with C-Flex vs standard CPAP, p < 0.01 paired t test). APAP with C-Flex eliminates sleep disordered breathing as effectively as standard CPAP. Patients indicated a preference for APAP with C-Flex suggesting a possible advantage in terms of patient adherence for this mode of treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".