Postoperative pain management education during the surgery core rotation at McMaster University, Waterloo Regional Campus.
Bibliographic record
Abstract
Background: Opioid over-prescription continues to be a challenge in the postoperative setting for management of acute pain. Initiatives have been developed to standardize postoperative opioid prescribing with an emphasis on multimodal pain management. However, there is a concern medical education has not remained current on this topic. Objective: The aim of this study is to explore current teaching around postoperative pain management during the surgery core rotation at McMaster University, Waterloo Regional Campus (WRC), and identify any opportunities for improvement. Methods: A 13-item survey was developed to determine effectiveness of teaching around postoperative pain management during the surgery core and its alignment with current guidelines. The survey was disseminated to third year medical students at the WRC. Results: Seven of nine respondents indicated that teaching on postoperative pain management and opioid reduction strategies was provided during the surgery core. All respondents receiving this teaching also indicated learning about a multimodal pain control approach consistent with current guidelines. However, only three of seven respondents noted receiving teaching on providing patient and caregiver education around the pain management plan, despite a strong recommendation in guidelines in favour of this practice. Conclusions: Most students receive teaching on multimodal postoperative pain management and opioid reduction strategies during the surgery core at the WRC. Opportunities to strengthen the teaching include addressing the role of patient and caregiver education in the pain management plan as well as incorporating the topic into formal teaching such as classroom sessions or learning objectives in the surgery core. Keywords: postoperative; opioids; multimodal pain management; medical education; surgery
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.003 |
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".