Medical Driving Assessment Outcomes in Seniors Using The KSCAr+Drive: An In-Office Screening Tool to Assist Clinicians in Determining Driving Safety and Who to Refer for Medical Driving Assessments
Bibliographic record
Abstract
Objectives: The purpose of this study was to examine how the KSCAr might be utilized to help identify seniors with MCI/Dementia as safe vs. unsafe to drive, or for whom referral to a medical driving assessment was required; and more specifically, if a subset of the KSCAr subtests could generate clinical evidence to support driving retirement or the need for further driving assessment.Methods: Thirty patients from two Ontario Geriatric programs (Kingston and London) who were referred for and completed a Medical driving assessment (DriveABLE) received a cognitive assessment that included the KSCAr.KSCAr scores were compared between those who passed/failed the road test.The KSCAr subtests that differentiated between those who passed/failed the road test using t-tests were then selected to comprise the "Drive Score".Discriminant function analysis was used to determine optimum cutoff scores for three groups: "PASS", "FAIL" and "GREY ZONE" (where a road test was deemed appropriate).Results: Of the total sample, 41.4% failed the road test, including all female participants.Eight KSCAr sub-tests differentiated the PASS/FAIL groups, resulting in the Drive Score sub-scale.Optimal cut-off scores for each of PASS, FAIL and GREY ZONE groups were determined with the following prediction accuracy rates: PASS (89% accuracy), FAIL (100% accuracy), GREY ZONE (64.3%). Conclusions:The Drive Score, emerging from the short (20 minute) KSCAr dementia screen offers clinicians a rapid and easy way to include empirically-based outcomes into their consideration of whether their patients with dementia are safe/unsafe to drive or need to be referred for a medical driving assessment, based on the outcome of similar patients who completed a medical driving assessment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".