Comparison of therapy outcomes in patients with PAP therapy using a patient engagement tool via PAP device vs a therapy companion app
Bibliographic record
Abstract
Background: Assessed patient engagement tool (PET) is designed to improve adherence to positive airway pressure (PAP) therapy and can be delivered through PAP device or therapy companion app. When using the app, users can access supporting and troubleshooting content. This study compares adherence, usage, termination rates, and outcomes via PAP device or therapy companion app. Methods: This retrospective study included 89,093 European patients who responded to PET via PAP device (n= 28,692) or app (n = 60,401). Inverse probability weighting adjusted age, country, therapy mode, usage, leak rates and residual AHI. Outcomes included usage, residual AHI, mask leak, and termination. Results: At 365 days, app responders had more patients averaging ≥4 hrs/night than PAP device (65.35% vs. 59.16%; p<0.001) and greater usage (4.77 ± 2.54 vs. 4.41 ± 2.72 hrs/day; p<0.001). On days used, app users logged more hours (5.90 ± 1.81 vs. 5.71 ± 1.99; p<0.001). They also had lower median leak (3.68 vs. 4.27 L/min) and residual AHI (2.38 vs. 2.49 events/hr; p<0.001). Termination rate was lower in the app group (26.85%) vs. PAP device (33.71%; RR: 1.255, p<0.001), with longer time to termination (123 vs. 111 days; p<0.001). Conclusion: Patients responding to PET via app showed better outcomes than PAP device PET user. These findings highlight the potential of an app to optimize PAP therapy outcomes. erj;66/suppl_69/PA2871/F1 F1 F1
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".