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Record W2432350591 · doi:10.1213/ane.0000000000001035

Opioids and Sleep Apnea

2016· letter· en· W2432350591 on OpenAlexaffabout
Xiangning Fan, Sebastian Straube

Bibliographic record

VenueAnesthesia & Analgesia · 2016
Typeletter
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineOpioidObstructive sleep apneaCentral sleep apneaAnesthesiaContext (archaeology)Sleep apneaApneaHypopneaSleep (system call)PolysomnographyInternal medicine

Abstract

fetched live from OpenAlex

To the Editor We read with interest the recent review by Correa et al.1 on the relationship between opioid use and central sleep apnea. Given the frequent use of opioid medications to treat acutely painful conditions, the importance of this question extends beyond the domain of anesthesiology into the occupational context. The nature and magnitude of this association is also relevant to work-related injury and the possibility of workplace impairment resulting from untreated sleep-disordered breathing, including opioid-induced central sleep apnea. Walker et al.2 have previously demonstrated a linear relationship between increasing opioid dose and both central and obstructive apnea and hypopnea episodes. However, it is not clear whether a safe threshold opioid dose (in morphine equivalents per day) exists, below which opioid effects on control of respiration during sleep appear to be absent in the majority of patients, or, if present, do not appear to result in clinically significant sleep-disordered breathing. The literature suggests that both obstructive sleep apnea3,4 and excessive daytime sleepiness4,5 are associated with occupational injury and workplace incidents. It may be that a similar association with workplace events exists for central sleep apnea, although we are not aware of any studies that have specifically examined this potential. Regardless, both central and obstructive sleep apnea are common in patients on chronic opioid therapy, and the problems frequently coexist.6 Therefore, we propose that it should be routine to evaluate patients with identifiable and clinically significant sleep apnea related to opioid usage (whether central or obstructive) not only for perioperative complications but also with respect to risk in the workplace. Of note, the reported prevalence of both sleep-disordered breathing and central sleep apnea in the studies identified by Correa et al.1 is higher than in the general population, and morphine equivalent dosage of >200 mg/day appeared to be associated with ataxic breathing in 92% of patients and increased severity of both central sleep apnea and ataxic breathing. Therefore, patients on high-dose opioids represent an at-risk group of patients to whom specialized sleep and occupational medicine evaluation should be offered, and the perioperative evaluation may represent a unique opportunity to identify this patient subset. Among other things, this is consistent with the responsibilities of anesthesiologists in their roles as perioperative physicians. Xiangning Fan, BSc, MD Sebastian Straube, BM BCh, MA (Oxon), DPhil Division of Preventive Medicine Department of Medicine University of Alberta Edmonton, Alberta, Canada [email protected]ualberta.ca

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0040.001
Research integrity0.0100.015
Insufficient payload (model declined to judge)0.0060.004

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.

Opus teacher head0.015
GPT teacher head0.268
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations2
Published2016
Admission routes2
Has abstractyes

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