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Record W4400043638 · doi:10.1503/cjs.003723

Postdischarge opioid use after lumbar spine surgery among older adults in Ontario: a population-based cohort study

2024· article· en· W4400043638 on OpenAlexaffvenueabout
Ana Johnson, Francis Nguyen, Melissa Richardson, Sarah Rabi, Steve Mann, Ian Gilron, Jeff Yach, Brian Milne, Gerald A. Evans, Joel L. Parlow

Bibliographic record

VenueCanadian Journal of Surgery · 2024
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsMcMaster UniversityQueen's UniversityUniversity of Calgary
Fundersnot available
KeywordsMedicineOdds ratioMedical prescriptionConfidence intervalRetrospective cohort studyOpioidCohort studyEmergency medicineLogistic regressionCohortPopulationHealth careAnesthesiaSurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Prescription opioid use places a considerable economic burden on health care systems. Older patients undergoing surgical procedures for painful conditions commonly receive opioids pre- and postoperatively, and are susceptible to adverse reactions. This study explores predictors of prolonged postoperative opioid use among older patients after lumbar spine surgery and the consequences in terms of health care utilization and costs. METHODS: We conducted a retrospective population-based cohort study using Ontario administrative data from older adults undergoing spine surgery between 2006 and 2017. Data were analyzed from 90 days preoperatively to 1 year after hospital discharge, with last postoperative opioid prescriptions stratified into 90-day increments. We used multivariable ordinal logistic regression to identify predictors of long-term opioid use and generalized linear modelling to examine resource utilization and health care costs (2021 Canadian dollars). RESULTS: Of 15 109 patients included, 40.8% received preoperative opioid prescriptions. Preoperative opioid use strongly predicted prolonged postoperative use (odds ratio [OR] 4.47, 95% confidence interval [CI] 4.16-4.79), with 48.3% of patients who received preoperative opioids continuing to use opioids for longer than 9 months, relative to 12.7% of those without preoperative use. Several other risk factors for prolonged use were identified. Patients receiving long-term postoperative opioids incurred greater health care costs relative to those with opioids prescribed for fewer than 90 days (OR 1.49, 95% CI 1.44-1.54). CONCLUSION: Among older adults undergoing spine surgery, preoperative opioid use was a strong predictor of prolonged postoperative use, which was associated with increased health care costs. These results form an important baseline for future studies evaluating strategies to reduce opioid use targeting older surgical populations.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.168
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.233
Teacher spread0.218 · 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

Citations1
Published2024
Admission routes3
Has abstractyes

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