257 Identifying safe vs unsafe medically at-risk drivers through serial trichotomization
Bibliographic record
Abstract
<h3>Background</h3> Clinical test batteries to predict safe vs unsafe drivers are not accurate enough to exempt the on-road test for medically at-risk drivers. Applying serial trichotomization to clinical test batteries may reduce the need to test all at-risk drivers. <h3>Aims</h3> To examine whether serial trichotomization predicts pass/fail outcomes of a comprehensive driving evaluation (CDE) in medically at-risk drivers with physician-referrals for a CDE. <h3>Methods</h3> CDE data was collected retrospectively from two driver assessment clinics in Canada (n=143; mean age 69.3±14.1 years). Clinical tests included the Montreal Cognitive Assessment (MoCA), Trail Making A and B tests, and Useful Field of View subtests 1–3 (UFOV1–3), and a pass/fail or indeterminate (i.e., fail with lessons and retest) outcome on the CDE. Serial trichotomization involved performing a receiver operating characteristics curve for each clinical test to determine cut-points with 100% accuracy in predicting pass/fail outcomes. A funnel was created arranging the clinical tests in order of accuracy (i.e., most-least) for predicting pass/fail outcomes, and with each clinical test’s cut-points, determining pass/fail or indeterminate outcomes. <h3>Results</h3> Compared to participants’ CDE outcomes, serial trichotomization of the UFOV3, UFOV2, UFOV1, Trails B, MoCA, and Trails A predicted more pass (44% vs 33%) and fail outcomes (34.3% vs 27%) with fewer indeterminate outcomes (21.7% vs 40%). <h3>Conclusion</h3> Serial trichotomization more accurately identifies safe vs unsafe medically at-risk drivers, reducing the number of unnecessary on-road tests, driving assessor caseloads, and client wait times. <h3>Learning Outcome</h3> Describe the process of serial trichotomization of clinical tests predicting CDE outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".