<scp>Postoperative</scp> opioid use following head and neck endocrine surgery: A <scp>multi‐center</scp> prospective study
Bibliographic record
Abstract
Abstract Background Opioid abuse is widespread in North America and the over‐prescription of opioids are a contributing factor. The goal of this prospective study was to quantify over‐prescription rates, evaluate postoperative experiences of pain, and understand the impact of peri‐operative factors such as adequate pain counseling and use of non‐opioid analgesia. Methods Consecutive recruitment of patients undergoing head and neck endocrine surgery was undertaken from January 1st 2020 to December 31st 2021 at four Canadian hospitals in Ontario and Nova Scotia. Postoperative tracking of pain levels and analgesic requirements were employed. Chart review and preoperative and postoperative surveys provided information on counseling, use of local anesthesia, and disposal plans. Results A total of 125 adult patients were included in the final analysis. Total thyroidectomy was the most common procedure (40.8%). Median use of opioid tablets was 2 (IQR 0–4), with 79.5% of prescribed tablets unused. Patients who reported inadequate counseling (n = 35, 28.0%) were more likely to use opioids (57.2% vs. 37.8%, p < .05) and less likely to use non‐opioid analgesia in the early postoperative course (42.9% vs. 63.3%, p < .05). Patients who received local anesthesia peri‐operatively (46.4%, n = 58) reported less severe pain on average [2.86 (2.13) vs. 4.86 (2.19), p < .05] and used less analgesia on postoperative day one [0 MME (IQR 0–4) vs. 4 MME (IQR 0–8), p < .05]. Conclusion Over‐prescription of opioid analgesia following head and neck endocrine surgery is common. Patient counseling, use of non‐opioid analgesia, and peri‐operative local anesthesia were important factors in narcotic use reduction. Level of evidence Level 3.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".