Opioid Disposal Practices of Patients With Life-Limiting Cancers in an Outpatient Palliative Care Clinic: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Patients with life-limiting cancers are commonly prescribed opioids to manage pain, dyspnea, and cough. Proper prescription opioid disposal is essential to prevent poisonings and deaths. Objective: We examined opioid disposal practices of patients referred to a Canadian outpatient palliative care clinic (OPCC). The primary objective was to determine the prevalence of OPCC patients who did not routinely dispose their opioids. The secondary objectives were to examine their methods of opioid disposal and to identify patient characteristics associated with routine disposal of opioids. Design and Setting: This cross-sectional study involved a retrospective chart review of new, adult patients who were seen in a Canadian OPCC (September 2018–August 2019) and completed a survey about opioid-related constructs: source of prescriptions, use, storage, disposal, and knowledge about associated harms. Results: Among the 122 study participants, half (58/111, 52.3%) reported that they did not routinely dispose their opioids. The most common method of disposal was by giving them to pharmacists (69/88, 78.4%). Cannabis use (odds ratio [OR]: 3.7, 95% confidence interval [CI]: 1.1–11.8) and neuropathic medication use (OR: 3.0, 95% CI: 1.2–7.2) were positively associated with routine disposal of opioids. Conversely, reports of an increased amount of opioid use in the past six months were negatively associated with routine disposal of opioids (OR: 0.38, 95% CI: 0.16–0.88). Conclusion: The high prevalence of people with life-limiting illnesses who do not routinely dispose their opioids requires increased attention. Interventions, such as education, are needed to reduce medication waste and opioid-related harms.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".