Long-term opioid prescribing and healthcare encounters in metastatic cancer: observational population study
Bibliographic record
Abstract
BACKGROUND: Although opioids are effective for cancer pain management, long-term use may result in adverse effects which are understudied among patients with metastatic disease. OBJECTIVES: To describe long-term opioid prescribing among patients with metastatic cancer and investigate how long-term prescribing practices are associated with the incidence of opioid-related hospitalisations and emergency department visits. METHODS: This retrospective cohort study included all opioid-naïve patients diagnosed with solid metastatic cancer in Alberta, Canada from 2004 to 2017 who had ≥1 year of follow-up. Patients were identified and followed using linked administrative health data. Long-term prescribing was defined as receiving a ≥90-day supply of opioids with a <30-day gap in supply within a 180-day period. The incidence rate of opioid-related healthcare encounters was compared based on characteristics of long-term prescribing (timing, dosage, duration and concurrent medications). RESULTS: The study included 10 927 patients, 2521 (23%) of whom received long-term opioid prescribing. These practices became more common near the end of life, with 53% of cases initiated during patients' last year of life. Opioid-related healthcare encounters were experienced by 85 (3.4%) recipients of long-term prescribing. Higher dosage (p<0.001) and concurrent prescribing of anxiolytics (p=0.001), benzodiazepines (p=0.001), antidepressants (p=0.027) and neuroleptics (p<0.001) were associated with a higher incidence of opioid-related healthcare encounters. CONCLUSIONS: Long-term opioid prescribing is common, and patients receiving long-term prescriptions with high dosage or concurrent psychoactive medications may benefit from interventions aimed at reducing opioid-related adverse effects. Further research is needed to determine strategies to minimise opioid-related harms for these patients while providing appropriate pain and symptom management.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".