Opioid Use among Same‐Day Surgery Patients: Prevalence, Management and Outcomes
Bibliographic record
Abstract
OBJECTIVES: To determine whether the prevalence of opioid use among patients requiring elective same-day admission (SDA) surgery is greater than the 2.5% prevalence found in the general population. Secondary objectives were to assess compliance with expert recommendations on acute pain management in opioid-tolerant patients and to examine clinical outcomes. METHODS: A retrospective review of 812 systematically sampled adult SDA surgical cases between April 1, 2008 and March 31, 2009 was conducted. RESULTS: Among 798 eligible patients, 148 (18.5% [95% CI 15.9% to 21.2%]) were prescribed opioids, with 4.4% prescribed long-acting opioids (95% CI 3.0% to 5.8%). Use of opioids was most prevalent among orthopedic and neurosurgery patients. Among the 35 patients on long-acting opioids who had a high likelihood of being tolerant, anesthesiologists correctly identified 33, but only 13 (37%) took their usual opioid preoperatively while 22 (63%) had opioids continued postoperatively. Acetaminophen, nonsteroidal anti-inflammatory drugs and pregabalin were ordered preoperatively in 18 (51%), 15 (43%) and 18 (51%) cases, respectively, while ketamine was used in 15 (43%) patients intraoperatively. Acetaminophen, nonsteroidal anti-inflammatory drugs and pregabalin were ordered postoperatively in 31 (89%), 15 (43%) and 17 (49%) of the cases, respectively. No differences in length of stay, readmissions and emergency room visits were found between opioid-tolerant and opioid-naive patients. CONCLUSION: Opioid use is more common in SDA surgical patients than in the general population and is most prevalent within orthopedic and neurosurgery patients. Uptake of expert opinion on the management of acute pain in the opioid tolerant patient population is lacking.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".