Opioid prescribing practices following elective surgery in Otolaryngology-Head & Neck Surgery
Bibliographic record
Abstract
BACKGROUND: Prescription opioid abuse has become a major issue across the world and especially in North America. Canada has the second highest number of opioid prescriptions per capita in the world, second only to the United States, with numbers continuing to rise in recent years. Surgeons play a critical role in this discussion as they are responsible for the management of post-operative pain in their patients. The objective of this study is to evaluate the opioid prescribing practices of Otolaryngologists-Head and Neck Surgeons in Canada and determine factors that may influence these practices. METHODS: The online survey was distributed to members of the Canadian Society of Otolaryngology-Head and Neck Surgery. Questions surveyed the respondents' demographics and opioid prescribing practices for common pediatric and adult elective surgeries. RESULTS: The survey was sent to 670 surgeons and trainees and 121 responses were received (18%). There was representation across all subspecialties with a mix of community and academic surgeons. The most commonly prescribed opioid was Codeine/Acetaminophen, 48.2% (n = 53), in the adult population, and Morphine, 47.1% (n = 41), in the pediatric population. The median total oral morphine equivalents prescribed across all adult surgeries was 123.75 mg (24.75 doses). The surgery with the highest oral morphine equivalents prescribed was tonsillectomy/adenoidectomy for both adult and pediatric patients, with a median of 150 mg (30 doses) for adults and 4.5 mg/kg (23 doses) for pediatrics. Gender, training years, year in residency, or reported level of conservatism did not predict the dose prescribed to either adult or pediatric patients. Due to the relatively low response rate, the generalizability of these results is unclear. CONCLUSIONS: Our study demonstrates a wide variability in opioid prescriptions across procedures and within each individual procedure. This variability reflects the lack of guidelines available for post-operative opioid prescribing and suggests that some Otolaryngologists may be prescribing higher doses of opioids than required. Opportunities for improving patient safety and resource stewardship regarding optimal prescribing practices should be explored.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".