Attitudes and self-reported practices of orthopedic providers regarding prescription opioid use
Bibliographic record
Abstract
OBJECTIVE: Orthopedic surgeons are the third-highest opioid prescribers in the United States. Their prescribing practices can significantly affect the quantity of unconsumed opioids available to fuel the current opioid epidemic. The aim of this study was to identify prescribing patterns and knowledge gaps among orthopedic providers for targeted future interventions and investigation. DESIGN: An online survey describing six common orthopedic surgical scenarios was distributed electronically to determine opioid type and quantity prescribed at discharge, medication disposal instructions, and the use of prescription drug monitoring programs (PDMPs) in the prescription writing process. SETTING: Tertiary care academic hospitals. PARTICIPANTS: Orthopedic physicians and mid-level providers practicing at Johns Hopkins Medical Institutions and University of Maryland Medical System. Of 179 providers contacted, 127 (71 percent) completed the survey. MAIN OUTCOME MEASURES: Quantity of opioid prescribed, utilization of PDMPs, and provision of opioid disposal instructions. RESULTS: While statistically significant associations were identified between quantity of opioid prescribed and surgical procedure, for five of six scenarios 95 percent of respondents recommended prescribing >55 oxycodone 5 mg pill equivalents (PEs) at discharge. An inverse correlation between years of clinical practice and mean number of PEs prescribed was observed. Fewer than 40 percent of respondents modified prescribing when presented with clinically relevant changes in scenario (history of depression or drug abuse). Over 60 percent of respondents do not use PDMPs, and 79 percent do not provide opioid disposal instructions. CONCLUSIONS: Our findings support a need for targeted education to mitigate the role of orthopedic postoperative prescribing practices on the current opioid abuse epidemic.
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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 | 0.000 |
| 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 | 0.001 |
| 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".