Clinical Utility of Office‐Based Cognitive Predictors of Fitness to Drive in Persons with Dementia: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To perform a systematic review of evidence available regarding in-office cognitive tests that differentiate safe from unsafe drivers with dementia. DESIGN: A comprehensive literature search of multiple databases including Medline, CINAHL, PsychInfo, AARP Ageline, and Sociofile from 1984 to 2005 was performed. This was supplemented by a search of Current Contents and a review of the bibliographies of all relevant articles. SETTING: English prospective cohort, retrospective cohort, and case-control studies that used accepted diagnostic criteria for dementia or Alzheimer's disease and that employed one of the primary outcomes of crash, simulator assessment, or on-road assessment were included. PARTICIPANTS: Two reviewers. MEASUREMENTS: The reviewers independently assessed study design, main outcome of interest, cognitive tests, and population details and assigned a Newcastle-Ottawa quality assessment rating. RESULTS: Sixteen articles met the inclusion criteria. Tests recommended by guidelines (e.g., the American Medical Association (AMA) and Canadian Medical Association guidelines) for the assessment of fitness to drive did not demonstrate robustly positive findings (e.g., Mini-Mental State Examination, Trails B) or were not evaluated in any of the included studies (e.g., Clock Drawing). Fifteen studies did not report any cutoff scores. CONCLUSION: Without validated cutoff scores, it is impossible to employ tests in a standardized fashion in front-line clinical settings. This study identified a research gap that will prevent the development of evidence-based guidelines. Recommendations to address this gap are that driving researchers routinely perform cutoff score analyses and that stakeholder organizations (e.g., AMA, American Geriatrics Society) sponsor consensus fora to review driving research methodologies.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".