Development of a decision-making tool for reporting drivers with mild dementia and mild cognitive impairment to transportation administrators
Bibliographic record
Abstract
BACKGROUND: Driving in persons with dementia poses risks that must be counterbalanced with the importance of the care for autonomy and mobility. Physicians often find substantial challenges in the assessment and reporting of driving safety for persons with dementia. This paper describes a driving in dementia decision tool (DD-DT) developed to aid physicians in deciding when to report older drivers with either mild dementia or mild cognitive impairment to local transportation administrators. METHODS: A multi-faceted, computerized decision support tool was developed, using a systematic literature and guideline review, expert opinion from an earlier Delphi study, as well as qualitative interviews and focus groups with physicians, caregivers of former drivers with dementia, and transportation administrators. The tool integrates inputs from the physician-user about the patient's clinical and driving history as well as cognitive findings, and it produces a recommendation for reporting to transportation administrators. This recommendation is translated into a customized reporting form for the transportation authority, if applicable, and additional resources are provided for the patient and caregiver. CONCLUSIONS: An innovative approach was needed to develop the DD-DT. The literature and guideline review confirmed the algorithm derived from the earlier Delphi study, and barriers identified in the qualitative research were incorporated into the design of the tool.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".