Predicting the risk of motor vehicle crash in the first year after cardioverter-defibrillator implantation
Bibliographic record
Abstract
BACKGROUND: Baseline health and driving data might allow clinicians to personalise medical driving restrictions after implantable cardioverter-defibrillator (ICD) implantation. METHODS: Using 22 years of population-based administrative data from British Columbia, Canada, we identified licensed drivers with a first ICD implantation between 1998 and 2018. After stratifying by ICD indication (primary vs secondary prevention of sudden cardiac death), we used baseline health and driving data and logistic regression to estimate each driver's 1-year crash risk. We assessed optimism-corrected discrimination and calibration of the final model using 200 bootstrapped samples. RESULTS: In the first year after implantation, there were 352 crashes among 3652 primary prevention ICD recipients and 270 crashes among 3408 secondary prevention ICD recipients. Crash prediction models exhibited poor discrimination (c-statistics 0.60 and 0.61, respectively) but good calibration (calibration slopes 1.14 and 1.07). The strongest predictors of crash among primary prevention ICD recipients were male sex, active vehicle insurance in the past year and the number of crashes in the past year. The strongest predictors of crash among secondary prevention ICD recipients were male sex, no history of seizure, an active prescription for opioids and active vehicle insurance in the past year. CONCLUSIONS: Crash prediction models based on health and driving data had a limited ability to distinguish individuals who subsequently crashed from individuals who did not. Observed crash risks are likely to be strongly influenced by unobserved changes in road exposure (the hours or miles of driving per week), limiting the application of these risk scores by clinicians and policy-makers.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".