Positive Airway Pressure Therapy Initiation and Continued Benzodiazepine Use Among Chronic Drug Users
Bibliographic record
Abstract
Treating obstructive sleep apnea (OSA) has been shown to improve concomitant insomnia symptoms, but whether treating OSA translates into reducing sedative medication use is unknown. We evaluated the association between initiating incident positive airway pressure (PAP) therapy and continued benzodiazepine drug receipt among chronic benzodiazepine users. This was a retrospective, population-based cohort study, analysing Ontario health administrative data from January 1, 2012-March 31, 2020. Persons aged 18 years and older, who were chronic benzodiazepine users, were included. The association of new PAP receipt on benzodiazepine drug discontinuation was evaluated at 3-9 months. Propensity score matching was used to account for potential differences in 40 relevant covariates between new and non-PAP users to minimise bias. We identified 249,516 chronic benzodiazepine users, of whom 10,688 (4.3%) newly received PAP. In the matched cohort, there was no significant difference in benzodiazepine discontinuation between new PAP and non-PAP users at 3-9 months follow-up (8.2% vs. 8.3%, relative risk [RR] 0.98, 95% confidence interval [CI] 0.90-1.07). New PAP receipt was not observed to influence stopping benzodiazepines at 3-9 months after PAP initiation. Therefore, our findings raise some uncertainty about the potential effectiveness of administering PAP therapy to improve concomitant insomnia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".