Analgesic prescribing habits and patterns of Canadian chief urology residents: A national survey
Bibliographic record
Abstract
INTRODUCTION: Prior studies have identified significant knowledge gaps in acute and chronic pain management among graduating urology residents as of five years ago. Since then, there has been increasing awareness of the impact of excessive opioid prescribing on long-term narcotic use and development of adverse narcotic-related events. However, it is unclear whether the attitudes and experience of graduating urology residents have changed. We set out to evaluate the attitudes and experience of graduating urology residents in prescribing opioid/non-opioid analgesia for acute (AP), chronic non-cancer (CnC), and chronic cancer (CC) pain. METHODS: Graduating urology residents were surveyed at a review course in 2018. The survey consisted of open-ended and close-ended five-point Likert scale questions. Descriptive statistics, Mann-Whitney U-test, and Student's t-test were performed. RESULTS: A total of 32 postgraduate year-5 (PGY5) urology residents completed our survey (92% response rate). The vast majority agreed that formal training in managing AP/CnC/CC is valuable (91/78/81%). Most find their training in CnC/CC management to be inadequate and are unaware of any opioid prescribing guidelines; 66% never counsel patients on how to dispose of excess opioids. In general, 88% are comfortable prescribing opioids, whereas most are very uncomfortable prescribing cannabis or antidepressants (100% and 78%, respectively). Residents reported the acute pain service as the highest-rated resource for information, and dedicated textbooks the least. CONCLUSIONS: This survey demonstrated that experience in pain management remains variable among urology residents. Knowledge gaps remain, particularly in the management of CC/CnC pain.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".