Using Serial Trichotomization With Common Cognitive Tests to Screen for Fitness to Drive
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to illustrate the use of serial trichotomization with five common tests of cognition to achieve greater precision in screening for fitness to drive. METHOD: We collected data (using the Montreal Cognitive Assessment, Motor-Free Visual Perception Test, Clock-Drawing Test, Trail Making Test Part A and B [Trails B], and an on-road driving test) from 83 people referred for a driving evaluation. We identified cutpoints for 100% sensitivity and specificity for each test; the driving test was the gold standard. Using serial trichotomization, we classified drivers as either "Pass," "Fail," or "Indeterminate." RESULTS: Trails B had the best sensitivity and specificity (66.3% of participants correctly classified). After applying serial trichotomization, we correctly identified the driving test outcome for 78.3% of participants. CONCLUSION: A screening strategy using serial trichotomization of multiple test results may reduce uncertainty about 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".