Motor vehicle crash risk after cardioverter-defibrillator implantation: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Limited empirical evidence informs driving restrictions after implantable cardioverter-defibrillator (ICD) implantation. We sought to evaluate real-world motor vehicle crash risks after ICD implantation. METHODS: We performed a retrospective cohort study using 22 years of population-based health and driving data from British Columbia, Canada (2019 population: 5 million). Individuals with a first ICD implantation between 1997 and 2019 were age and sex matched to three controls. The primary outcome was involvement as a driver in a crash that was attended by police or that resulted in an insurance claim. We used survival analysis to compare crash risk in the first 6 months after ICD implantation to crash risk during a corresponding 6-month interval among controls. RESULTS: A crash occurred prior to a censoring event for 296 of 9373 individuals with ICDs and for 1077 of 28 119 controls, suggesting ICD implantation was associated with a reduced risk of subsequent crash (crude incidence rate, 8.5 vs 10.5 crashes per 100 person-years; adjusted HR (aHR), 0.71; 95% CI 0.61 to 0.83; p<0.001). Results were similar after stratification by primary versus secondary prevention ICD. Relative to controls, ICD patients had more traffic contraventions in the 3 years prior to ICD implantation but fewer contraventions in the 6 months after implantation, suggesting individuals reduced their road exposure (hours or miles driven per week) or drove more conservatively after ICD implantation. CONCLUSIONS: Crash risk is lower in the 6 months after ICD implantation than among matched controls, likely because individuals reduced their road exposure in order to comply with contemporary postimplantation driving restrictions. Policymakers might consider liberalisation of postimplantation driving restrictions while monitoring crash rates.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".