CANDRIVE OLDER DRIVER STUDY: OBJECTIVE VARIABLES PREDICTIVE OF AT-FAULT COLLISION
Bibliographic record
Abstract
One of the aims of the Candrive Prospective Older Driver Study was to prospectively identify older drivers who were medically at risk for driving. 928 active older drivers were recruited across 7 Canadian sites and followed up to 7 years. The primary outcome was at-fault collision. Using Generalized Estimating Equations univariate analysis revealed 81 variables with a significance of p<0.1. Multivariate analysis of objective variables with adjustment for driving exposure resulted in a model where 6 variables including MVPT correct responses (p=0.008), previously being pulled over by police (p=0.023), previous crash involvement (p=0.006), left rapid foot taps (p=0.024), left wrist extension strength (p=0.020) and overall calmness (p=0.023) significantly contributed to identifying medically at risk older drivers. With further ongoing analysis these variables will assist in deriving an objective risk stratification tool that can be used by clinicians in an outpatient clinic setting.
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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 | 0.002 |
| 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) | 0.001 | 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".