Postdischarge opioid use after lumbar spine surgery among older adults in Ontario: a population-based cohort study
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".