Opioid Prescribing after Surgery among Chronic Opioid users in Ontario: A Population-based Cohort Study
Bibliographic record
Abstract
Patients who chronically use opioids may undergo surgery; it is unclear if surgery alters the trajectory of opioid consumption in these patients. We sought to determine if exposure to surgery is associated with opioid discontinuation among chronic users, and factors associated with opioid discontinuation after surgery. The study included 4,755 surgical and 14,265 matched non-surgical patients with chronic opioid use. After adjusting for patient characteristics, surgery was associated with an increased likelihood of opioid discontinuation (aHR: 1.34 95%CI: 1.27, 1.42). Among surgical patients, factors associated with a reduced odds of discontinuation included a mean preoperative opioid dose >90 morphine milligram equivalents (aOR: 0.39 95%CI:0.31, 0.49), preoperative oxycodone prescriptions (aOR: 0.74 95%CI:0.55, 0.98), and a diagnosis of chronic obstructive pulmonary disease (aOR: 0.75 95%CI: 0.64, 0.88) or dementia (aOR: 0.58 95%CI: 0.37, 0.91). Further research is needed to evaluate interventions that can influence post-operative opioid discontinuation, particularly in high risk 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".