THE CANDRIVE/OZCANDRIVE PROSPECTIVE OLDER DRIVER COHORT STUDY RESULTS
Bibliographic record
Abstract
This symposium will describe results for the Candrive/Ozcandrive Prospective Older driver Study. The Candrive study involves 928 actively driving older adults (age 70 and above) who were recruited across 7 Canadian sites to participate in a 5 year prospective study of older drivers. The linked Ozcandrive study includes 257 older drivers (age 75 and older) from Australia and New Zealand. All participants had comprehensive annual assessments and driving patterns were monitored using in car recording devices with GPS tracking capabilities. This symposium will confirm that the Canadian population of older drivers recruited is similar to the older Canadian driving population by comparison with the Canadian Community Health Survey. Changes in driving patterns over the course of the study for the Australian Ozcandrive participants will be desribed. Similarly, for Canadian drivers results will be presented in relation to preparation and readiness to transition from active driving to cessation. This cohort provided the unique opportunity to link driver reaction time measured thorugh the Attention Network Test to traffic violations where it was demonstrated that drivers with faster reaction times had higher rates of traffic violation. Finally, the investigators will report on the predictors for at-fault collisions in older drivers that will ultimately contribute to the derivation of an older driver risk stratification tool.
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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 | 0.003 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".