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Journal Article

Citation

Krasniuk S, Crizzle AM. Inj. Prev. 2022; 28(Suppl 2): A39-A40.

Copyright

(Copyright © 2022, BMJ Publishing Group)

DOI

10.1136/injuryprev-2022-safety2022.119

PMID

unavailable

Abstract

Proceedings of the 14th World Conference on Injury Prevention and Safety Promotion (Safety 2022)

Background 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.

Aims 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.

Methods 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.

Results 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%).

Conclusion 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.
Learning Outcome Describe the process of serial trichotomization of clinical tests predicting CDE outcomes.


Language: en

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