Opioid Medication Disposal Among Patients Following Hand Surgery
Bibliographic record
Abstract
Background: Despite increased public awareness to dispose of unused narcotics, opioids prescribed postoperatively are retained, which may lead to drug diversion and abuse. This study assessed retention of unused opioids among hand surgery patients and describes disposal methods and barriers. Methods: Participants undergoing hand surgery were given an opioid disposal information sheet preoperatively (N = 222) and surveyed postoperatively to assess disposal or retention of unused opioids, disposal methods, and barriers to disposal. A binomial logistic regression was conducted to assess whether age, sex, pain intensity, and/or the type of procedure were predictors of opioid disposal. Results: There were 171 patients included in the analysis (n = 51 excluded; finished prescription or continued opioid use for pain control). Unused opioids were retained by 134 patients (78%) and disposal was reported by 37 patients (22%). Common disposal methods included returning opioids to a pharmacy (49%) or mixing them with an unwanted substance (24%). Reasons for retention included potential future use (54%), inconvenient disposal methods (21%), or keeping an unfilled prescription (9%). None of the patient factors analyzed (age, sex, type of procedure performed, or pain score) were predictors of disposal of unused narcotics ( P > .05). Conclusions: Most patients undergoing hand surgery retained prescribed opioids for future use or due to impractical disposal methods. The most common disposal methods included returning narcotics to a pharmacy or mixing opioids with unwanted substances. Identifying predictors of disposal may provide important information when developing strategies to increase opioid disposal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".