Abstract 4: A Simple Way to Reduce Opioid Over-Prescribing by Plastic Surgery Residents
Bibliographic record
Abstract
BACKGROUND: Over-prescribing following surgery is a known contributor to the opioid epidemic, increasing the risk of opioid abuse and diversion. Trainees are the primary prescribers of these medications at academic institutions, and we previously identified over-prescribing in this population across the US and Canada. We hypothesized that a simple “intervention” could improve over-prescribing METHODS: All plastic surgery trainees at one institution completed an anonymous survey querying opioid-prescriber education, factors contributing to prescribing practices, and analgesic prescriptions written after eight common procedures. Oral morphine equivalents (OME) were calculated for each procedure. A simple 4-hour intervention was then administered to all residents in 1-hour sessions, during grand rounds over several weeks: (1) screening of HBO’s documentary “Warning: This Drug May Kill You”, (2) St. Louis Police Department on opioid abuse and crime; (3) experienced psychologist on treating patients with opioid addictions; (4) Pain Management Anesthesiologist on the basic science of pain and analgesia. Surveys were repeated several months after completing the intervention. Pre- and post-intervention prescriptions were compared using either students t-test or Mann-Whitney U tests, depending on data normality as determined by the Shapiro-Wilks test RESULTS: Response rate was >90% on both surveys. For all but 2 procedures (carpal tunnel release and abdominoplasty), there was a statistically significantly decrease in prescribed MME after the intervention. Residents more frequently adjusted prescriptions to specific procedures (from 53% to 95%, p=0.002). There was no change in the number of residents adhering to the “one-prescriber rule” (37% to 50%, p=0.408). CONCLUSION: A largely passive intervention can improve over-prescribing by plastic surgery residents. Coordination of care issues remain a problem, such as following the “one-prescriber rule.” The intervention can be easily adopted: (1) it simply raised awareness – no mandatory prescribing protocols. (2) Three of the 4 hours are available, to other training programs, now – and for free: Two of the 3 hours of in-person lectures were converted to Prezi’s and narrated, and are available here: https://surgicaleducation.wustl.edu/. The HBO supports public screenings of its documentary. Further work can define (1) minimums for interventions to “raise awareness”, and (2) the role post-op protocols play in reducing over-prescribing.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.004 | 0.001 |
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; both teacher heads agree on what is shown here.
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".