Rates and risk factors for persistent opioid use after cardiothoracic surgery: A cohort study
Bibliographic record
Abstract
BACKGROUND: This study's aim was to estimate potential risk factors for persistent opioid use after cardiothoracic surgery. METHODS: This study included participants in the McGill University Health Centre clinical trial (2014 to 2016). Provincial medical services, prescription claims, and medical charts data were linked. Persistent opioid use was defined as an initial peri-operative opioid dispensation followed by an opioid dispensation between 91 and 180 days postdischarge. Multivariable Cox Proportional Hazards models were used to assess factors associated with persistent opioid use. RESULTS: A cohort of 815 patients (mean age: 68.9 [standard deviation = 8.9]) was assembled, of which 8.2% became persistent opioid users. Factors such as higher Charlson Comorbidity Index (adjusted hazard ratio: 3.4, 95% confidence interval: 1.1-10.6), history of diabetes (adjusted hazard ratio: 2.1, 95% confidence interval: 1.3-3.4), substance and alcohol abuse (adjusted hazard ratio: 16.3, 95% confidence interval: 5.3-49.5), and radiotherapy (adjusted hazard ratio: 2.4, 95% confidence interval: 1.5-4.1) were associated with a higher hazard of persistent opioid use. Previous opioid use (adjusted hazard ratio: 1.7, 95% CI: 1.0-2.8), daily peri-operative opioid dose (adjusted hazard ratio: 2.3, 95% confidence interval: 1.5-3.7), having an opioid dispensation 30 days pre-admission (adjusted hazard ratio: 1.7, 95% confidence interval: 1.0-2.8), and pre-admission analgesic use (adjusted hazard ratio: 1.7, 95% confidence interval: 1.0-2.8), were also associated with an increased hazard of persistent use. Being prescribed multimodal analgesia at discharge (adjusted hazard ratio: 0.54, 95% confidence interval: 0.32-0.92) was associated with a 46% decreased hazard of developing persistent opioid use. CONCLUSION: Multiple patient- and medication-related characteristics were associated with an increased hazard of persistent opioid use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".