Mandatory reporting by physicians of patients potentially unfit to drive.
Bibliographic record
Abstract
BACKGROUND: One strategy for the prevention of motor vehicle crashes is physician reporting of medically unfit drivers to vehicle licensing authorities, as mandated by law in Ontario, Canada. We studied drivers involved in life-threatening crashes who required hospital admission to determine how many had previously been seen and reported by a physician in the community. METHODS: We identified consecutive drivers involved in a crash who were admitted to Canada's largest trauma centre between 30 June 1996 and 30 June 2001 to assess the prevalence of 3 chronic medical conditions reportable to vehicle licensing authorities (alcohol abuse, cardiac disease, and neurological disorders). We then conducted a case series analysis of linked health and transportation databases to determine how many drivers had previously been seen and reported by a physician in the community. RESULTS: A total of 1,605 injured drivers were identified, of whom 37% had a reportable condition (95% confidence interval [CI] 35-39). Those with a reportable condition had made a total of 20,505 previous visits to 2,332 physicians during the five years before the crash. The majority of patients with a reportable condition (85%, 95% CI 82-88) had seen a physician in the year before the crash but few (3%, 95% CI 2-4) had been reported to licensing authorities. Alcohol abuse was the most common underlying reportable condition (prevalent in 72% of trauma patients with a reportable condition) and the least common reason for a previous report (reported in 2% of those with a reportable condition). INTERPRETATION: Unsafe drivers often visit physicians and yet are rarely reported to licensing authorities even under mandatory reporting laws for preventive medical reporting.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".