Has Province-Wide Symptom Screening Changed Opioid Prescribing Rates in Older Patients With Cancer?
Bibliographic record
Abstract
PURPOSE: Previous work in Ontario demonstrated that 33% of patients with cancer with severe pain did not receive opioids at the time of their pain assessment. With efforts to increase symptom screening and management since then, the objective of this study was to examine temporal trends in opioid prescribing. METHODS: The cohort was comprised of Ontario residents ≥ 65 years of age with a cancer history who were eligible for the government pharmacare program and had a pain assessment using the Edmonton Symptom Assessment System. Use of the Edmonton Symptom Assessment System is part of a provincial initiative to screen ambulatory patients with cancer for symptoms. Annually between 2007 and 2013, we used the date of an individual's highest pain score as the index date to calculate annual opioid prescription rates for claims within 30 days before and up to 7 days after the index date. A logistic regression model evaluated the association between index year and odds of receiving an opioid prescription. RESULTS: During the study period, the number of individuals undergoing symptom assessment annually increased more than eight-fold. Opioid prescription rates were directly related to pain scores, but there was an annual 5% relative decrease in the odds of receiving an opioid prescription during the era from 2009 to 2013. CONCLUSION: We are doing better at screening for pain, but this has not led to an increase in analgesic intervention for those identified. Additional work is required to determine what opioid prescribing rate is optimal to ensure we are not missing opportunities to improve patient comfort.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".