Association between a recalled positive airway pressure device and incident cancer: a population-based study
Bibliographic record
Abstract
Background The real-world consequences of a Philips Respironics recall for positive airway pressure (PAP) devices distributed between 2009 and 2021 are unknown. Methods We conducted a retrospective population-based study using health administrative databases (Ontario, Canada) on all new adult PAP users identified through the provincial funding system, free of cancer at baseline, who initiated (claimed) PAP treatment between 2012 and 2018. Everyone was followed from the PAP claim date to the earliest of incident cancer diagnosis, death or end of follow-up (March 2022). We used inverse probability of treatment weighting to balance baseline characteristics between individuals on recalled devices and those on devices from other manufacturers. Weighted hazard ratios of incident cancer were compared between groups. Results Of 231 692 individuals identified, 58 204 (25.1%) claimed recalled devices and 173 488 (74.9%) claimed devices from other manufacturers. A meaningful baseline difference between groups (standardised difference ≥0.10) was noted only by location-relevant covariates; other variables were mostly equally distributed (standardised differences ≤0.06). Over a median (interquartile range) follow-up of 6.3 (4.9–8.0) years, 11 166 (4.8%) developed cancer: unadjusted rates per 10 000 person-years of 78.8 (95% CI 76.0–81.7) in the recall groupversus74.0 (95% CI 72.4–75.6) in others (p=0.0034). Propensity score weighting achieved excellent balance in baseline characteristics between groups (standardised differences ≤0.07). On a weighted sample, there was no statistical difference in the hazard of incident cancer between groups: cause-specific hazard ratio (recalledversusothers) 0.97 (95% CI 0.89–1.06). Conclusion In our real-world population study, compared to other manufacturers and adjusting for confounders, recalled Philips Respironics PAP devices do not appear to be independently associated with developing cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".