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Record W4383533539 · doi:10.3389/frsle.2023.1161857

Association between opioid prescription profiles and adverse health outcomes in opioid users referred for sleep disorder assessment: a secondary analysis of health administrative data

2023· article· en· W4383533539 on OpenAlexafffund
Tetyana Kendzerska, Atul Malhotra, Andrea S. Gershon, Marcus Povitz, Daniel I. McIsaac, Shawn D. Aaron, Gregory L. Bryson, Robert Talarico, Michael Godbout, Peter Tanuseputro, Frances Chung

Bibliographic record

VenueFrontiers in Sleep · 2023
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsBruyèreUniversity of CalgaryUniversity Health NetworkUniversity of TorontoSunnybrook Health Science CentreHealth Sciences CentreOttawa HospitalUniversity of Ottawa
FundersUniversity of TorontoPhysicians' Services Incorporated FoundationOttawa Hospital Research Institute
KeywordsMedicineOxycodoneHazard ratioOpioidMedical prescriptionConfidence intervalHydromorphoneOpioid use disorderEmergency medicineEmergency departmentInternal medicineAdverse effectPsychiatryPharmacology

Abstract

fetched live from OpenAlex

Background: Information is needed to guide safe opioid prescribing in adults referred for a sleep disorder assessment. Previous studies have shown that individuals referred for a sleep disorder assessment have a higher likelihood of long-acting opioids and higher opioid dosages prescription than the general population, suggesting that these individuals are more at risk for opioid-related adverse health consequences. Methods: = 15,713). Through provincial health administrative databases, individuals were followed over time to assess the association between opioid use characteristics and 1-year all-cause mortality, hospitalizations and emergency department (ED) visits, and opioid-related hospitalizations and ED visits within extended follow-up to 2018. Results: Controlling for covariates, chronic opioid use (vs. not) was significantly associated with increased hazards of all-cause mortality [adjusted hazard ratio(aHR): 1.84; 95% confidence interval (CI): 1.12-3.02], hospitalization (aHR: 1.14; 95% CI: 1.02-1.28) and ED visit (aHR: 1.09; 95% CI: 1.01-1.17). A higher opioid dosage [morphine equivalent daily dose (MED) >90 vs. ≤ 90 mg/day] was significantly associated with increased hazards of all-cause or opioid-related hospitalization (aHR: 1.13; 95% CI: 1.02-1.26 and aHR: 2.27; 95% CI: 1.53-3.37, respectively). Morphine or hydromorphone prescription (vs. oxycodone) was significantly associated with an increased hazard of all-cause hospitalization (aHR: 1.30; 1.07-1.59 and aHR: 1.43; 95% CI: 1.20-1.70, respectively). Hydromorphone or fentanyl prescription (vs. oxycodone) was significantly associated with an increased hazard of opioid-related ED visit and/or hospitalization (aHR: 2.28, 95% CI: 1.16-4.47 and aHR: 2.47, 95% CI: 1.16-5.26, respectively). Conclusion: Findings from this retrospective study may inform the safe prescribing of opioids in adults referred for a sleep disorder assessment.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.049
GPT teacher head0.373
Teacher spread0.324 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2023
Admission routes2
Has abstractyes

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