Using Serial Trichotomization with Neuropsychological Measures to Inform Clinical Decisions on Fitness-to-Drive among Older Adults with Cognitive Impairment
Bibliographic record
Abstract
Decisions related to driving safety and when to cease driving are complex and costly. There is an interest in developing an off-road driving test utilizing neuropsychological tests that could help assess fitness-to-drive. Serial trichotomization has demonstrated potential as it yields 100% sensitivity and specificity in retrospective test samples. The purpose of this study was to test serial trichotomization using four common neuropsychological tests (Trail Making Test Part A and B, Clock Drawing Test, and Modified Mini-Mental State Examination). Test scores from 105 patients who were seen in a memory clinic were abstracted. After applying the model, participants were classified as unfit, fit, or requiring further testing, 38.1%, 25.8%, and 36.1%, respectively. This study provides further evidence that trichotomization can facilitate the assessment of fitness-to-drive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".