Associations Between Physician Prescribing Behavior and Persistent Postoperative Opioid Use Among Cancer Patients Undergoing Curative-intent Surgery
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the association between prescribers' opioid prescribing history and persistent postoperative opioid use in cancer patients undergoing curative-intent surgery. BACKGROUND: Study has shown that patients may be over-prescribed analgesics after surgery. However, whether and how the prescriber's opioid prescribing behavior impacts persistent opioid use is unclear. METHODS: All adults with a diagnosis of solid cancers who underwent surgery during the study period (2009-2015) in Alberta, Canada and were opioid-naïve were included. The key exposure was the historical opioid-prescribing pattern of a patient's most responsible prescriber. The primary outcome was "new persistent postoperative opioid user," was defined as a patient who was opioid-naïve before surgery and subsequently filled at least 1 opioid prescription between 60 and 180 days after surgery. RESULTS: We identified 24,500 patients. Of these, 2106 (8.6%) patients became a new persistent opioid user after surgery. Multivariate analysis demonstrated that patients with most responsible prescribers that historically prescribed higher daily doses of opioids (≥50 vs <50 mg oral morphine equivalent) had an increased risk of new persistent opioid use after surgery (odds ratio = 2.41, P < 0.0001). In addition to the provider's prescribing pattern, other factors including younger age, comorbidities, presurgical opioid use, chemotherapy, type of tumor/surgical procedure were also found to be independently associated with new persistent postoperative opioid use. CONCLUSIONS: Our results suggest that prescriber with a history of prescribing a higher opioid dose is an important predictor of persistent postoperative opioid use among cancer patients undergoing curative-intent surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".