Comprehensive Analysis of Opioid Use After Common Elective Outpatient Orthopaedic Surgeries
Bibliographic record
Abstract
BACKGROUND: Prescription opioid abuse remains an ongoing public health crisis, especially in orthopaedic surgery. The purpose of the present study is to analyze opioid-prescribing patterns and investigate risk factors for prolonged opioid use after common outpatient orthopaedic surgical procedures. METHODS: After institutional review board approval, a review of 1,384 patients undergoing common elective outpatient orthopaedic procedures from January 2018 to June 2019 was conducted. Data on controlled substance prescriptions were obtained from the prescription drug monitoring program website. Statistical analysis was done to identify predictors for a second opioid prescription and prolonged opioid use (>6 months). RESULTS: Over 10% (150/1,384) of patients were still using opioids beyond 6 months. Of the opioid exposed patients, 60.4% (174/288) filled at least 1 additional opioid prescription postoperatively, and 29.2% (84/288) filled prescriptions beyond 6 months, compared with 26.4% (289/1,096) and 6.0% (66/1,096) of opioid-naive patients, respectively. Following multivariate analysis, significant predictors for filling a second opioid prescription included preoperative opioid use, current smoker status, benzodiazepine use, psychiatric disorder, and advanced age. CONCLUSION: This study revealed risk factors for prolonged opioid use after orthopaedic surgery. Surgeons should be mindful of these risk factors and counsel patients regarding postoperative pain management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.001 | 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".