Comparison of patient-reported outcomes between alternative care provider-led and physician-led care for severe sleep disordered breathing: secondary analysis of a randomized clinical trial
Bibliographic record
Abstract
Abstract Background Previous research has suggested that alternative (respiratory) care providers (ACP) may provide affordable, accessible care for sleep-disordered breathing (SDB) that decreases wait-times and improves clinical outcomes. The objective of this study was to compare ACP-led and sleep physician-led care for SDB on patient reported outcome and experiences, with a focus on general and health-related quality of life, sleepiness, and patient satisfaction. Methods We conducted a secondary analysis of a randomized trial in which participants with severe SDB were assigned to either ACP-led or physician-led management. We created longitudinal linear mixed models to assess the impacts of treatment arm and timepoint on total and domain-level scores of multiple patient-reported outcome measures and patient-reported experience measures. Results Patients in both treatment arms (ACP-led n = 81; sleep-physician = 75) reported improved outcomes on the Sleep Apnea Quality of Life Index, Health Utilities Index, and Epworth Sleepiness Scale. Patients in each group had similar and clinically meaningful improvements on domains assessing cognition, emotion, and social functioning. The linear mixed models suggested no significant difference between treatment arms on the patient-reported outcomes. However, scores significantly improved over time. Conclusions Management of SDB using ACPs was comparable to physician-led care, as measured bypatient-reported outcome and experience measures. While loss to follow-up limits our findings, these results provide some support for the use of this novel health service delivery model to improve access to high quality SDB care. Clinical trial registration This is analysis of data from the study registered Clinicaltrials.gov (NCT02191085).
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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 | 0.023 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".