The effects of multiple medical conditions on the risk of Quebec drivers being involved in a motor vehicle crash
Bibliographic record
Abstract
Three major studies have examined the influence of medical conditions upon crash risk. (Monash report, Charlton et al (2010); Vaa (2003); Vernon et al (2002)). These large studies reveal that some medical conditions are associated with higher risk of an adverse driving event, crashes and infractions, than others. A previous paper of the authors on crash risk for Quebec drivers with a medical condition demonstrated that there are increases to crash risk according to the number of medical conditions that are present (see also 01528271). In this paper, this phenomenon is investigated in more detail: The objective of this phase of the Quebec study is to examine the effects of multiple medical conditions on the risk of drivers having a crash involving injury or death. The results revealed that female drivers with multiple medical conditions have crash risks that are consistently higher than their male counterparts. For female drivers the incremental increase in crash risk for each additional medical condition is double that for males with the same number of medical conditions. However, when comparing males to females it should be borne in mind that the crash risk for a male with no medical conditions is 1.60 times greater than his female equivalent. Thus, although the female drivers may have higher odds ratios, the number of crashes in which they are involved may be fewer than those of their male counterparts. Contrary to the other age groups, the crash risk does not increase for the over-65 age-group with more than 3 conditions. These results underline the importance for licensing agencies of identifying younger drivers with multiple medical conditions.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".