Effect of preoperative long-term opioid therapy on patient outcomes after total knee arthroplasty: an analysis of multicentre population-based administrative data
Bibliographic record
Abstract
Background: Up to 40% of patients are receiving opioids at the time of total knee arthroplasty (TKA) in the United States despite evidence suggesting opioids are ineffective for pain associated with arthritis and have substantial risks. Our primary objective was to determine whether preoperative opioid users had worse knee pain and physical function outcomes 12 months after TKA than patients who were opioid-naive preoperatively; our secondary objective was to determine the prevalence of opioid use before and after TKA in Alberta, Canada. Methods: In this retrospective analysis of population-based data, we identified adult patients who underwent TKA between 2013 and 2015 in Alberta. We used multivariable linear regression to examine the association between preoperative opioid use and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain and physical function scores 12 months after TKA, adjusting for potentially confounding variables. Results: Of the 1907 patients, 592 (31.0%) had at least 1 opioid dispensed before TKA, and 124 (6.5%) were classified as long-term opioid users. Long-term opioid users had worse adjusted WOMAC pain and physical function scores 12 months after TKA than patients who were opioid-naive preoperatively (pain score β = 7.7, 95% confidence interval [CI] 4.0 to 11.6; physical function score β = 7.8, 95% CI 4.0 to 11.6; p < 0.001 for both). The majority (89 ([71.8%]) of patients who were long-term opioid users preoperatively were dispensed opioids 180-360 days after TKA, compared to 158 (12.0%) patients who were opioid-naive preoperatively. Conclusion: A substantial number of patients were dispensed opioids before and after TKA, and patients who received opioids preoperatively had worse adjusted pain and functional outcome scores 12 months after TKA than patients who were opioidnaive preoperatively. These results suggest that patients prescribed opioids preoperatively should be counselled judiciously regarding expected outcomes after TKA.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".