Opioid prescribing after thyroid and parathyroid surgery: A survey of North American surgeons
Bibliographic record
Abstract
OBJECTIVES: Opioid overprescribing remains an issue following thyroid and parathyroid surgery (TPS). We performed a cross-sectional survey study to describe opioid prescribing trends of otolaryngology-head and neck surgeons across North America. METHODS: We performed a cross-sectional survey study of otolaryngology-head and neck surgeons who are members of the Canadian Society of Otolaryngology-Head and Neck Surgery (CSO) or the American Head and Neck Society (AHNS). The voluntary 20-item online survey addressed surgeon analgesia practices for TPS and was distributed from February 2023-July 2024. Statistical analysis included descriptive methods, multivariable logistic regression, and Chi-square testing. RESULTS: Overall, 153 surgeons completed the survey (response rate: 22.6 %) and of these surgeons, most were Canadian, fellowship-trained, and practicing for 0-10 years. Most surgeons (73 %) rated postoperative patient pain as 3-5/10. Over 75 % of surgeons prescribed opioids for inpatient thyroid surgery with early-career surgeons more likely to prescribe opioids and US surgeons were less likely to prescribe opioids. Oxycodone was commonly prescribed by US surgeons and Canadian surgeons preferred codeine. Canadian surgeons were likelier to prescribe opioids, especially ≥20 opioid tabs, when compared to US surgeons. Almost 50 % of surgeons prescribed 10-19 opioid tabs despite predicting that postoperative patients likely only use 0-10 opioid tabs. CONCLUSIONS: Otolaryngology-head and neck surgeons routinely prescribe opioids for TPS despite identifying that patients only consume a fraction of their opioid prescription. Standardization of opioid prescribing and promotion of multimodal analgesia practices are needed to reduce opioid overprescription.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".