Association of providers’ prescribing patterns with postsurgical opioid use among cancer patients undergoing curative-intent surgery.
Bibliographic record
Abstract
6530 Background: Patients with cancer are vulnerable to chronic opioid use. Although opioid use may be appropriate, preliminary data suggest that a significant proportion may be using opioids inappropriately. This study aims to evaluate the association between the history of the providers’ opioid-prescribing patterns and post-surgical opioid use in cancer patients undergoing curative-intent surgery. Methods: This population-based study included all patients diagnosed with common solid tumors who received curative-intent surgery and were non-opioid users prior to surgery between 2009 and 2015 in Alberta, Canada. Based on previously published methods, a new persistent opioid user was defined as opioid-naïve prior to surgery and who subsequently filled at least one opioid prescription between 60 and 180 days after surgery. The opioid-prescribing patterns of a patient’s most responsible provider (MRP) were measured as the mean daily dosage (oral morphine equivalent, OME) that was prescribed to all other patients by that provider prior to the surgical date. Multivariable logistic regression was performed to identify associations between the MRP’s prescribing patterns and the patient’s opioid use after surgery. Results: 14,780 patients met the inclusion criteria and were associated with 2,880 MRPs, among which 2,364 (16%) patients became new persistent opioid users after surgery. Multivariate analysis demonstrated that patients with MRPs who routinely prescribed higher doses of opioids (≥60 vs. 0-59 mg OME: OR = 2.33, P < 0.0001) for their patients were associated with a greater risk of new persistent opioid use after surgery. In addition, those with a higher Charlson comorbidity index (P = 0.006), visited more prescribers (P < 0.0001), had a specific tumor type (breast, colorectal, lung, prostate, melanoma or kidney vs. others, P < 0.0001), received adjuvant chemotherapy (OR = 1.37, P < 0.0001), and received adjuvant radiation (OR = 1.3, P = 0.0004) were also associated with greater risk of new persistent opioid use after surgery. Conclusions: Our results suggest that prescribers with a history of prescribing higher opioid doses are an important predictor of chronic opioid use among cancer patients undergoing curative-intent surgery. Awareness of physician prescribing practices and their unintended consequences may inform strategies to minimize persistent post-operative opioid use in cancer patients.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".