Association Between Opioid-Related Mortality and History of Surgical Procedure: A Population-Based Case-Control Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 it