Opioid Prescription After Pain Assessment: A Population-Based Cohort of Elderly Patients With Cancer
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to measure opioid prescription (OP) rates in elderly cancer outpatients around the time of assessment for pain and to evaluate factors associated with receiving OPs for those with severe pain. PATIENTS AND METHODS: The cross-sectional cohort includes all patients with cancer in Ontario older than age 65 years who completed a pain assessment as part of a provincial initiative of systematic symptom screening. Patients were assigned to mutually exclusive categories by pain score severity: 0, 1 to 3 (mild), 4 to 6 (moderate), and 7 to 10 (severe). We linked multiple provincial health databases to examine the proportion of patients with an OP within 7 days after or 30 days before the assessment date. We examined factors associated with OPs for patients with pain scores of 7 to 10. RESULTS: The proportion of patients with an OP increased as pain score severity increased: 10% of those with no pain, 24% of those with mild pain, 45% of those with moderate pain, and 67% of those with severe pain. More specifically, for those with severe pain, 41% filled an OP within 7 days of assessment for pain, and 26% had an OP from the 30 days before assessment for pain, leaving 33% without an OP. In multivariable analysis, factors associated with OPs are younger age, male sex, comorbid illness, cancer type, and assessment at home. CONCLUSION: Despite a generous time window for capturing OPs, the proportion of patients without an OP seems high. Further knowledge translation is required to maximize the impact of the symptom screening initiative in Ontario and to optimize management of cancer-related pain.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".