The potential for diversion of prescribed opioids among orthopaedic patients: Results of an anonymous patient survey
Bibliographic record
Abstract
INTRODUCTION: Diversion of prescription opioid medication is a contributor to the opioid epidemic. Safe handling practices can reduce the risk of diversion. We aimed to understand: 1) if orthopaedic patients received instructions on how to safely handle opioids, 2) their typical storage/disposal practices, and 3) their willingness to participate in an opioid disposal program (ODP). METHODS: Cross-sectional study of adult orthopaedic patients who completed an anonymous survey on current or past prescription opioid use, instruction on handling, storage and disposal practices, presence of children in the household, and willingness to participate in an ODP. Frequencies and percentages of responses were computed, both overall and stratified by possession of unused opioids. RESULTS: 569 respondents who reported either current or past prescription opioid use were analyzed. 44% reported receiving storage instructions and 56% reported receiving disposal instructions from a health care provider. Many respondents indicated unsafe handling practices: possessing unused opioids (34%), using unsafe storage methods (90%), and using unsafe disposal methods (34%). Respondents with unused opioids were less likely to report receiving handling instructions or using safe handling methods, and 47% of this group reported having minors or young adults in the household. Respondents who received storage and disposal instructions were more likely to report safe storage and disposal methods. Seventy-four percent of respondents reported that they would participate in an ODP. CONCLUSION: While many orthopaedic patients report inadequate education on safe opioid handling and using unsafe handling practices, findings suggest targeted education is associated with better behaviours. However, patients are willing to safely dispose of unused medication if provided a convenient option. These findings suggest a need to address patient knowledge and behavior regarding opioid handling to reduce the risk of opioid diversion.
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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".