ATTITUDES AND PRACTICES SURROUNDING OPIOID PRESCRIPTIONS FOLLOWING OPEN REDUCTION INTERNAL FIXATION OF DISTAL RADIUS AND ANKLE FRACTURES: A SURVEY OF THE CANADIAN ORTHOPAEDIC ASSOCIATION MEMBERSHIP
Bibliographic record
Abstract
The past two decades have seen a significant increase in consequences associated with non-medical misuse of prescription opioids, such as addiction and unintentional overdose deaths. The purpose of this study was to design and disseminate an electronic survey to assess attitudes and opioid-prescribing practices of Canadian orthopaedic surgeons and trainees following open reduction internal fixation (ORIF) of distal radius and ankle fractures. This study was first to assess these factors following ORIF of distal radius and ankle fractures using a survey design. A 40-item survey was developed focusing on four themes: Respondent Demographics, Opioid-Prescribing Practice, Patients with Substance Use Disorders, and Drug Diversion. The survey was distributed among members of the Canadian Orthopaedic Association. Descriptive statistics were used to summarize respondent demographics and outcomes of interest. A Chi-square test was used to determine if the proportion of opioid prescriptions between attending surgeons and surgeons in-training was equal. One-hundred and ninety-one surveys were completed. Most respondents prescribed 10-40 tabs of immediate-release opioids, though this number varied considerably. While most respondents believed patients consumed only 40-80% of the prescribed opioids (73.6%), only 28.7% of respondents counselled patients on safe storage/disposal of leftover opioids. Thirty-point-five percent of respondents felt confident in their knowledge of opioid use and mechanisms of addiction. Most respondents desired further education on topics such as procedure-based opioid-prescribing protocols (74.2%), alternative pain management strategies (69.7%), and mechanisms of opioid addiction (49.0%). The principle finding of this study is the apparent lack of a standardized approach to post-operative opioid prescribing in distal radius and ankle fractures, illustrated by the wide range in number of opioids prescribed by Canadian orthopaedic surgeons. Our data suggest a trend towards over-prescription among respondents following distal radius and ankle ORIF, which is similar to the statistics observed among United States orthopaedic surgeons. Future studies should use the trends identified in this study to inform and rationalize interventions targeted at reducing post-operative opioid prescribing for common orthopaedic trauma procedures, especially in Canada where the opioid epidemic continues to worsen.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".