Impact of patient engagement tool on PAP therapy outcomes
Bibliographic record
Abstract
Background: A patient engagement tool incorporating subjective feedback from the first 30 days was developed to support PAP therapy. This study evaluated the impact of PET engagement on PAP usage, discontinuation, residual AHI, and mask leak at 365 days vs. no engagement. Methods: This retrospective analysis included 63,873 connected PAP users (age 51.8 ± 12.7 years; 27.4% female) across Europe. Patients were grouped by level of engagement in the first 30 days: Full (baseline + all additional check-ins), Partial (Baseline and/or First 30 Days) and no engagement. Inverse probability weighting adjusted for age, gender, country, therapy mode, usage, leak rates and residual AHI. Average usage, residual AHI, mask leak, and CPAP termination at 365 days were compared. Results: At 1 year, the fully engaged group showed higher average usage (5.21 ± 2.32 vs. 4.51 ± 2.71hrs/day; <0.001) and a greater percentage of patients averaging ≥4 hrs/night (72.7% vs. 60.7%, p<0.001) compared to the not engaged group. Mask leak was lower (p=0.004), while residual AHI was similar (p=0.518). 1 year CPAP termination risk was 46% lower (RR: 0.56) (p<0.001) for the fully engaged group compared to the not engaged group. Conclusion: Patients fully engaged with PET in the first 30 days of PAP therapy show better adherence and lower termination rates in the first year than non-engaged patients. These findings underscore digital tool's potential to improve long-term PAP therapy outcomes. erj;66/suppl_69/PA2869/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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".