Cardioverter-Defibrillator Implantation as a Risk Factor For Motor Vehicle Crash
Bibliographic record
Abstract
BACKGROUND: Limited empirical evidence informs fitness-to-drive recommendations after implantable cardioverter-defibrillator (ICD) implantation. Cohort designs can be deceptive because ICD recipients differ from control individuals and may temporarily cease driving after implantation. OBJECTIVES: This study sought to generate evidence to inform medical driving restrictions after ICD implantation. METHODS: We used population-based data to identify all drivers involved in a serious motor vehicle crash in British Columbia, Canada, from 1997 to 2019. Exposure was defined as ICD implantation in the 6 months before a crash. One analysis used a case-crossover design to control for relatively fixed individual characteristics like driving experience. Another analysis used a responsibility design to account for road exposure (miles of driving per week). Both analyses used logistic regression with adjustment for potential confounders. RESULTS: In the case-crossover analysis of crash-involved ICD recipients, ICD implantation occurred in 212 of 3,299 precrash intervals and in 485 of 6,598 control intervals, suggesting no temporal association between ICD implantation and subsequent crash (6.4% vs 7.4%; adjusted OR [aOR]: 0.86; 95% CI: 0.71-1.03; P = 0.11). In the analysis of all crash-involved drivers with determinate crash responsibility, 14 of 22 drivers with recent ICD implantation and 532,741 of 1,035,433 drivers without recent ICD implantation were deemed responsible for their crash, suggesting no association between ICD implantation and crash responsibility (crude proportion responsible, 64% vs 51%; aOR: 2.20; 95% CI: 0.94-5.30; P = 0.08). CONCLUSIONS: The 6-month interval after ICD implantation is not associated with increased odds of crash nor with increased likelihood of crash responsibility. Contemporary driving restrictions in the first weeks after ICD implantation appear to adequately mitigate the potential increase in crash risk.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".