Patient Perceptions Towards Reduction or Avoidance of Opioids After Knee and Hip Arthroplasty: A Cross-Sectional Survey
Bibliographic record
Abstract
Background and objective Opioid analgesics are routinely prescribed to manage pain after total joint arthroplasty (TJA) but are associated with several adverse effects. There is a scarcity of literature exploring patients' receptivity, attitudes, and perceptions towards opioid-sparing postoperative protocols. In light of this, we conducted this study to address those gaps in the literature. Methods We administered a cross-sectional survey to patients aged 18 years or older who were attending either a preoperative or a postoperative TJA appointment, up to 12 months after surgery. We aimed to determine the proportion of patients who would be open to receiving opioid-free or opioid-reduced postoperative care, identify patient characteristics associated with receptivity, and determine patients' perceptions regarding the efficacy and safety of opioids. We constructed multivariable logistic regression models to explore features associated with patients' receptivity to opioid reduction or avoidance. Results We approached 200 patients, and 190 returned a complete survey. A quarter of respondents believed that other analgesics were similarly effective or superior to opioids, and 68% perceived that opioids were associated with more side effects than alternatives. Of note, 50% of patients indicated that they would be receptive to reduced opioid use postoperatively. Patients' receptivity was associated with not using opioids at the time of survey completion [odds ratio (OR): 2.5, 95% confidence interval (CI): 1.04-6.4), and the belief that opioids had more side effects than alternatives (OR: 3.4, 95% CI: 1.5-7.9); 40% of respondents indicated they would be willing to avoid opioid use after surgery, and receptivity was associated with the belief that opioids cause more side effects than alternatives (OR: 4.3, 95% CI: 1.8-11.9) and that non-opioid analgesics are similarly or more effective (OR: 3.4, 95% CI: 1.4-8.3). Conclusions Many participants were willing to reduce or avoid the use of postoperative opioids, and receptivity was strongly associated with beliefs regarding the comparative benefits and harms of alternatives. These findings suggest opportunities to reduce the use of opioids after TJA and enhance patient education to address misconceptions.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".