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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".