The Introduction of a New Screening Tool for the Identification of Cognitively Impaired Medically At-Risk Drivers
Bibliographic record
Abstract
UNLABELLED: The number of drivers with a cognitive impairment due to dementia or other age-associated pathologies will increase significantly over the next 3 decades. Physicians are well placed to identify medically at-risk drivers, but are hampered by the lack of a valid, easy to administer screening tool. This research develops and validates a brief screening tool for use in the primary care setting to identify drivers with cognitive impairment with or without dementia. Initial Study Participants: A cohort of 146 consecutive referrals from community-based family physicians, diagnosed with an undifferentiated cognitive impairment or dementia, as well as 35 community dwelling healthy controls. Validation Study: A cohort of 192 consecutive referrals carrying the same diagnosis as above and 52 community dwelling healthy controls. Criterion Measure: Pass/fail on an On-Road evaluation. Predictor Measures: Subtests of the DemTect, a screening test for cognitive impairment or dementia developed by Kalbe and colleagues.(1) Initial Study: Three of the DemTect measures predicted On-Road outcomes (R(2) = .262). Regression results were used to develop a simple scoring algorithm, with cut-points then derived by identifying those most at risk for failing and passing the On-Road assessment, and those needing a driving assessment for determination of driving competency. 89 individuals scored in the indeterminate range, with 49 and 43 predicted to fail and pass, respectively-86% and 84% of those predicted to fail and pass did subsequently fail and pass. Validation Study: 123 individuals scored in the indeterminate range, with 66 and 55 predicted to fail and pass, respectively-80% and 87% of those predicted to fail and pass did subsequently fail and pass. CONCLUSIONS: The SIMARD A Modification of the DemTect ( S creen for the I dentification of cognitively impaired M edically A t- R isk D rivers) is a brief paper and pencil screening tool with a high degree of accuracy that can be used for immediate decisions in the clinical setting.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".