Impact of a mandatory physician reporting system for cardiac patients potentially unfit to drive.
Bibliographic record
Abstract
CONTEXT: Sudden cardiac incapacitation of a driver may lead to the death or serious injury of passengers or bystanders. This has raised public safety concerns and has led to the creation of legislation to protect the public. Some jurisdictions in Canada and the United States have introduced mandatory physician reporting of patients who may be unfit to drive for medical reasons. The impact on motor vehicle accident (MVA)-related morbidity and mortality of mandatory physician reporting for at-risk cardiac patients is unknown. OBJECTIVE: To determine the impact of mandatory physician reporting legislation (for cardiac patients) in Ontario (population 10.3 million) on MVA-related morbidity and mortality. DATA SOURCES: Reporting data were obtained from the Ontario Ministry of Transportation. Incidence and prevalence data were taken from Ontario Ministry of Health sources and from the literature (MEDLINE). Data for modelling were taken from the literature (MEDLINE) and from the Canadian Cardiovascular Society's Consensus Conference document on cardiac illness and fitness to drive. DATA EXTRACTION: Licence suspension data (correlated with medical illness) were taken directly from government documents. These were then applied to a 'risk of harm' formula used to calculate the risk posed to bystanders and passengers by the suspended patients if they had continued to drive. Canadian licence suspension guidelines were then reviewed in conjunction with cardiac disease incidence and prevalence data to arrive at the number of patients who should have been suspended. Physician compliance with the legislation was then calculated, along with the potential impact on MVA-related morbidity and mortality in the scenario of 100% physician compliance. STUDY SELECTION: All Ontario drivers who had licence suspensions in 1996 for reasons of cardiac disease were included in the analysis. DATA SYNTHESIS: Nine hundred and ninety-four licences were suspended for cardiac reasons in 1996, compared with an estimated 72,407 that should have been suspended if Canadian guidelines had been followed (1.4%). Less than one death or serious injury was avoided as a result of the legislation (from the 'risk of harm' formula). If all drivers with cardiac illness had been suspended from driving, up to 29.2 such events could potentially have been avoided. However, only 13 of 929 (1.4%) road fatalities in Ontario in 1996 were attributed to a driver with a medical illness. CONCLUSIONS: Mandatory physician reporting of patients with cardiac illness has a negligible impact on MVA-related morbidity and mortality.
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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 | 0.001 |
| 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 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".