Cognitive basis about risk level classifications for the self-assessment of older drivers
Bibliographic record
Abstract
[Purpose] This study analyzed the cognitive functions according to risk level for the Driver 65 Plus measure, and examined the cognitive basis of self-assessment for screening the driving risk of elderly drivers. [Subjects and Methods] A total of 46 older drivers with a driver's license participated in this study. All participants were evaluated with Driver 65 Plus. They were classified into three groups of "safe," "caution" and "stop," and examined for cognitive functions with Trail Making Test and Montreal Cognitive Assessment-K. The cognitive test results of the three groups were compared. [Results] Trail Making Test-A, Trail Making Test-B, and Montreal Cognitive Assessment-K showed a significant difference between the three groups. The safe group showed significantly higher ability than the caution and stop groups in the three cognitive tests. In addition, cognitive functions of naming, attention, language, and delayed recall were significantly different between the three groups. [Conclusion] Self-assessment of older drivers is a useful tool for screening the cognitive aspects of driving risk. The cognitive functions, such as attention and recall, are the critical factors for screening the driving risk of elderly drivers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".