Long-term opioid use in seniors following hip and knee arthroplasty in Ontario: a historical cohort study
Bibliographic record
Abstract
PURPOSE: Canadian seniors who undergo hip and knee arthroplasty often experience significant postoperative pain, which could result in persistent opioid use. We aimed to document the impact of preoperative opioid use and other characteristics on postoperative opioid prescriptions in elderly patients following hip and knee replacement before widespread dissemination of opioid reduction strategies. METHODS: We conducted a historical cohort study to evaluate postoperative opioid use in patients over 65 yr undergoing primary total hip and knee replacement over a ten-year period from 1 April 2006 to 31 March 2016, using linked de-identified Ontario administrative data. We determined the use of preoperative opioids and the duration of postoperative opioid prescriptions (short-term [1-90 days], prolonged [91-180 days], chronic [181-365 days], or undocumented). RESULTS: The study included 49,638 hip and 85,558 knee replacement patients. Eighteen percent of hip and 21% of knee replacement patients received an opioid prescription within 90 days before surgery. Postoperatively, 51% of patients filled opioid prescriptions for 1-90 days, while 24% of hip and 29% of knee replacement patients filled prescriptions between 6 and 12 months, with no impact of preoperative opioid use. Residence in long-term care was a significant predictor of chronic opioid use (hip: odds ratio [OR], 2.64; 95% confidence interval [CI], 1.93 to 3.59; knee: OR, 2.46; 95% CI, 1.75 to 3.45); other risk factors included female sex and increased comorbidities. CONCLUSION: Despite a main goal of joint arthroplasty being relief of pain, seniors commonly remained on postoperative opioids, even if not receiving opioids before surgery. Opioid reduction strategies need to be implemented at the surgical, primary physician, long-term care, and patient levels. These findings form a basis for future investigations following implementation of opioid reduction approaches.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".