In Response
Bibliographic record
Abstract
We thank Fan et al.1 for their letter regarding our review.2 The purpose of our review was to educate anesthesiologists about the prevalence, risk factors, mechanism, and perioperative considerations for patients with sleep-disordered breathing (SDB) associated with chronic opioid therapy for noncancer pain. It was not within the scope of our review to discuss the occupational hazards associated with chronic opioid use and untreated SDB. We agree that the association of opioid-induced obstructive sleep apnea (OSA) with central sleep apnea (CSA) in relation to occupational injury and workplace incidents is an important issue that should be studied further. OSA and CSA frequently coexist in patients on chronic opioid therapy.2,3 In our review, the prevalence of SDB in all reported groups being treated with chronic opioids, even partial μ-agonists was 70% (42%–85%), and CSA was 24%. The prevalence was higher than reported in the general population; this is likely attributable to most patients being specifically referred for evaluation of suspected sleep apnea. Although we reported a positive correlation of dose of opioids (morphine equivalent daily dose >200 mg/day) with the risk for SDB,2 we did not mean to imply that a dose of <200 mg/day was the threshold for safety. Multiple factors may be responsible for an increase in severity of opioid-induced CSA, such as individual susceptibility because of μ-receptor polymorphism, pharmacokinetics related to body weight, and interactions with concurrent drugs. In fact, there is no established level of opioids at which an increased risk for clinically significant respiratory effects occurs. In the study cited by Fan et al.1 regarding the association of excessive daytime sleepiness and OSA in relation to occupational injury, use of opioids or any concomitant medications among workers is not reported.4–6 Thus, the impact of opioids on SDB and work-related injuries is unclear from these previous studies. Polysomnography was performed in only 1 study,5 and the sleep assessment was based on a sleep questionnaire, Epworth Sleepiness Scale, Mini Sleep Questionnaire, and snoring frequency in the other studies.4,6 However, we agree that the routine screening for excessive daytime sleepiness and SDB in individuals on chronic opioid therapy—particularly those on high-dose opioids—may be warranted in the workplace. Unfortunately, there is no clinical tool available to screen CSA, and none of the current screening instruments has been validated or found useful for identifying patients with SDB related to chronic opioids. Anesthesiologists and chronic pain physicians should refer high-risk individuals, who are identified in preoperative evaluations or in chronic pain clinics, to a sleep medicine specialist for further evaluation. Further research is needed in understanding the effect of chronic opioids on SDB and occupational injuries and workplace safety for patients living with chronic pain. Denis Correa, MBBS, MD Department of Anesthesiology Toronto Western Hospital University Health Network University of Toronto Toronto, Ontario, Canada Robert J. Farney, MD University of Utah Health Sciences Center Intermountain Sleep Disorders Center Intermountain Healthcare LDS Hospital Salt Lake City, Utah Frances Chung, MBBS, FRCPC Arun Prasad, MBBS, FRCA, FRCPC Department of Anesthesiology Toronto Western Hospital University Health Network University of Toronto Toronto, Ontario, Canada David Lam, BMSc Jean Wong, MD, FRCPC Department of Anesthesiology Toronto Western Hospital University Health Network University of Toronto Toronto, Ontario, Canada [email protected]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.235 | 0.117 |
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".