Patient level factors associated with chronic opioid use in cancer patients.
Bibliographic record
Abstract
11580 Background: Opioid prescribing in oncology is increasingly scrutinized given public health concerns about chronic opioid use, misuse, and harms. We aimed to evaluate patient reported pain scores, mental health indicators, prior opioid use, and number of opioid prescribers as potential risk factors for chronic opioid use in a large Canadian province. Methods: This was a population-based cohort study using administrative health data of patients in Alberta, Canada, diagnosed between Jan 2016 and Jan 2017, and completed a prospective comprehensive symptom survey within +/- 60 days of diagnosis. Patients were divided into two groups: chronic opioid use (COU) (defined as continuous prescriptions for opioids for at least 90 days post diagnosis) and non-chronic opioid use (NCOU). Logistic regression models were used to evaluate factors associated with COU. Results: We included 694 patients. Most had breast (20%), colorectal (13%), and lung (33%) cancers. There were no differences in mean age (65 years) or gender (50% female) between the groups. In total, 32% had moderate to high pain scores at diagnosis. Of the 14% with COU, 79% were opioid naïve at diagnosis. Those in the COU group were more often diagnosed with advanced stage of disease (66% vs 40%), had lung cancer (47%), and were opioid tolerant at diagnosis (defined as > 90 days of continuous opioids within 1 year prior to their diagnosis) (21% vs 3%). In comparison, 64% of COU versus 27% of NCOU had moderate to severe pain scores at diagnosis (p < 0.001). COU had significantly higher anxiety and depression scores at diagnosis versus NCOU (p = 0.004). Among patients with COU, morphine equivalent daily doses increased from 27.3 (pre-diagnosis) to 65.1 (post-diagnosis). Irrespective of treatment type or stage, those who had moderate to high pain scores, were opioid tolerant at diagnosis, or had multiple prescribers were at greater risk for COU (see Table). Conclusions: Specific patient groups were at increased risk of COU and should be the focus of adaptive prescribing approaches to ensure that opioid use is appropriate. [Table: see text]
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".