Association Between Opioid-Related Mortality and History of Surgical Procedure: A Population-Based Case-Control Study
Bibliographic record
Abstract
Objective: This study examined whether there is an association between opioid-related mortality and surgical procedures. Methods: A case-control study design using deceased controls compared individuals with and without opioid death and their exposure to common surgeries in the preceding 4 years. This population-based study used linked death and hospitalization databases in Canada (excluding Quebec) from January 01, 2008 to December 31, 2017. Cases of opioid death were identified and matched to 5 controls who died of other causes by age (±4 years), sex, province of death, and date of death (±1 year). Patients with HIV infection and alcohol-related deaths were excluded from the control group. Logistic regression was used to determine if there was an association between having surgery and death from an opioid-related cause by estimating the crude and adjusted odds ratios (ORs) with the corresponding 95% confidence interval (CI). Covariates included sociodemographic characteristics, comorbidities, and the number of days of hospitalization in the previous 4 years. Results: We identified 11,865 cases and matched them with 59,345 controls. About 11.2% of cases and 12.5% of controls had surgery in the 4 years before their death, corresponding to a crude OR of 0.89 (95% CI: 0.83-0.94). After adjustment, opioid mortality was associated with surgical procedure with OR of 1.26 (95% CI: 1.17-1.36). Conclusions: After adjusting for comorbidities, patients with opioid mortality were more likely to undergo surgical intervention within 4 years before their death. Clinicians should enhance screening for opioid use and risk factors when considering postoperative opioid prescribing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".