Concomitant benzodiazepine and opioids decrease sleep apnoea risk in chronic pain patients
Bibliographic record
Abstract
Background The concurrent use of sedating centrally acting drugs and opioids by chronic pain patients occurs routinely despite concerns of negative impacts on respiration during sleep. The effects of centrally acting drugs and opioids on sleep apnoea have not been well characterised. The objective of this study was to assess the effect of concomitant centrally acting drugs and opioids on the prevalence and severity of sleep apnoea in chronic pain patients. Methods We conducted a prospective cohort study at five chronic pain clinics. Each participant underwent an in-laboratory polysomnography and daily morphine milligram equivalents were calculated. Participants were grouped into centrally acting drugs and opioid users versus sole opioid users. Results Of the 332 consented participants, 204 underwent polysomnography and 120 (58.8%) had sleep apnoea (72% obstructive, 20% central, and 8% indeterminate sleep apnoea). Overall, 35% (71 of 204) were taking opioids alone, and 65% (133 of 204) were taking centrally acting drugs and opioids. There was a 69% decrease in the odds of having sleep apnoea (apnoea�hypopnoea index =5 events�h -1 ) in participants taking benzodiazepine/opioids versus sole opioid users (OR 0.31, 95% CI:0.12�0.80, p=0.015). Additionally, concomitant benzodiazepine/opioids versus sole opioid use was associated with a decrease in respiratory arousal index scores (p=0.03). Mean overnight S pO 2 was approximately 1% lower in the concomitant benzodiazepine/opioids group versus sole opioid users (93.1�2.5 versus 94.4�2.1%, p=0.01). Conclusion In chronic pain patients on opioids, administration of certain benzodiazepine sedatives induced a mild respiratory depression but paradoxically reduced sleep apnoea risk and severity by increasing the respiratory arousal threshold.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".