Road Exposure After Cardioverter-Defibrillator Implantation and its Potential Influence on Reported Motor Vehicle Crash Risks
Bibliographic record
Abstract
Many individuals transiently reduce their road exposure (kilometers or hours of driving per month) after receiving an implantable cardioverter-defibrillator (ICD). This markedly influences interpretation of monthly crash risks, but very few studies describe real-world road exposure after ICD implantation. We obtained 18 years of population-based health and driving data for drivers undergoing ICD implantation in British Columbia, Canada. We estimated drivers' monthly "road exposure relative to baseline" (RERB) after ICD implantation (0 = complete cessation of driving; 1 = road exposure unchanged), using clinical data to infer the duration of compulsory driving restrictions, and using published data to account for incomplete adherence to restrictions and voluntary reductions in road exposure by month since implantation. We then used estimated RERB to calculate exposure-adjusted crash risks. Among 3,454 primary prevention ICD recipients, RERB-adjusted crash rate in the first month after implantation was not significantly different than among matched controls (mean recipient RERB = 0.29; adjusted incidence rate ratio [aIRR] = 2.22, 95% CI 0.72 to 6.87), but sensitivity analyses suggested that crash rate adjusted for a plausible lower-bound RERB estimate was ∼5-fold higher than among controls. Among 3,070 secondary prevention ICD recipients, RERB-adjusted crash rate in the first 6 months after implantation was not significantly different than among matched controls (mean recipient RERB = 0.50; aIRR = 1.11, 95% CI 0.77 to 1.61), but sensitivity analyses indicated that crash rate in the first 3 months after implantation adjusted for a plausible lower-bound RERB estimate was ∼2-fold higher than among controls. In conclusion, the substantial transient reductions in road exposure after ICD implantation should inform interpretation of monthly crash risks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".