Narcotic Prescriptions following Knee and Shoulder Arthroscopy: A Survey of the Arthroscopy Association of Canada
Bibliographic record
Abstract
Purpose Canada has the second-highest opioid use in the world. Despite knee and shoulder arthroscopy being among the most commonly performed orthopaedic procedures, there exists little guidelines for pain management. Methods A survey was developed and distributed to members of the Arthroscopy Association of Canada. The objectives were: to understand opioid prescribing patterns after knee and shoulder arthroscopy, to determine if surgeons believe opioid over-prescription is an issue and to identify other pain management strategies surgeons are regularly using. Results A total of 38 responses were included (38.3%). Eighty-two percent of surgeons felt opioid over-prescription was an issue in arthroscopic surgery. The average post-operative knee or shoulder arthroscopy prescription included a total of 156 +/- 84.4 (0-400) mg of oral morphine equivalents (OMEs). Less than one-third of respondents (29%) had received formal peri-operative pain management training. Fifty-five percent of respondents felt that non-opioid medications do not provide adequate pain relief after arthroscopic surgery. Nearly all respondents (95%) stated they would change their prescription practice if high-quality evidence were to suggest that they should do so. Conclusions The majority of respondents identified opioid over-prescription as a problem after arthroscopic surgery. Surgeons are prescribing five times the amount of OMEs to patients that previous literature suggests the median patient uses after arthroscopic knee surgery. Surgeons generally state they would reduce or eliminate opioid prescriptions to arthroscopy patients if high-level evidence were to emerge suggesting that adequate pain control could be achieved without the use of narcotics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".