Overprescription of opioid analgesia is common following ambulatory Otolaryngology—Head and Neck surgery procedures: A multicenter study
Bibliographic record
Abstract
Abstract Background The rise in the use of prescription opioids for postoperative analgesia within surgery has mirrored an increased trend of opioid‐related morbidity within Canada and the United States. This study prospectively studied daily pain levels and medication requirements postoperatively in patients undergoing elective Otolaryngology—Head and Neck surgery procedures. Methods Patients were asked to prospectively document their pain level and medication use daily for 7 days postoperatively. A final survey was used to quantify unused medication left at home and clarify each patient's disposal plan. We included patients undergoing elective outpatient or short stay surgeries from three tertiary care centers in Toronto, Ontario from September 2016 to September 2017. Previous opioids users or patients suffering from chronic pain were excluded. Results A final cohort of 56 eligible adult patients were included in the study. The most common procedures were thyroidectomy (n = 19), endoscopic sinus surgery (n = 10), tympanoplasty/ossiculoplasty (n = 7), and cochlear implant (n = 5). Most patients received a prescription for acetaminophen/codeine (n = 29, 51.8%) or acetaminophen/oxycodone (n = 22, 39.3%) and used on average 29% of their initial prescription. Patients most commonly opted to keep their unused narcotics at home (n = 23, 41%). A total of 710 tablets of narcotics were overprescribed in our study population, 351 of which were kept in patients' home for future use. Conclusion There is a clear tendency to overestimate postoperative pain resulting in significant overprescription of opioids among Otolaryngologists.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".