Trends in Outpatient Opioid Prescriptions for Cancer Pain Between 2016 and 2021
Bibliographic record
Abstract
PURPOSE Increasing opioid regulations have resulted in reduced opioid prescriptions, including for cancer pain, despite guideline exemptions. Data after 2017 following the Centers for Disease Control's 2016 pain management guidelines are limited on opioid prescribing practices of oncologists. The purpose of this study was to examine the trend in dose of opioids prescribed by oncologists to patients with cancer pain referred to outpatient palliative care between 2016 and 2021. METHODS A single-center, cross-sectional, retrospective study was conducted at a tertiary cancer center's outpatient palliative care clinic including 375 adult patients referred for initial consultation for cancer pain between 2016 and 2021. The main outcome was the trend in prescribed opioid doses, expressed as morphine-equivalent daily dose in mg/day. Additional analyses were conducted to identify predictors of opioid prescriptions. RESULTS The median age (range) was 61 (19-85), 50% were women, 67% were non-Hispanic White, 80% had advanced cancer, and 91% reported proficiency in English. Ninety-five percent had solid tumors, predominantly GI (22%), breast (15%), and genitourinary (14%). From 2016 to 2021, the median dose of opioids decreased from 37.5 to 7.5 ( P < .001). The proportion of patients on long-acting opioid decreased from 26% to 12% ( P = .019), whereas that of patients without opioids increased from 28% to 41% ( P = .008). CAGE-AID score (reflecting potential for substance abuse; β Coefficient, 43.2 [95% CI, 23.3 to 63.2], P < .001) and pain on the Edmonton Symptom Assessment Scale (5.77 [95% CI, 2.6 to 8.9], P < .001) predicted higher opioid dose, whereas non-English language predicted lower dose (–26.9 [95% CI, –53.1 to –0.8], P = .043). CONCLUSION During the study period, we observed a five-fold decline in opioid dose prescribed by oncologists for cancer pain. This raises concerns for undertreatment of pain in patients with cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".