Diagnosis of Alzheimer's dementia and vehicle driving restriction: a scoping review
Bibliographic record
Abstract
There are doubts about vehicle driving restriction for patients with Alzheimer's disease. A scoping review was carried out using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-ScR) methodology. Relevant databases were searched for articles published between 2000 and 2022 in English, Spanish, or Portuguese. Articles were included if they specifically addressed driving, risk of accidents, permission or licence to drive a motor vehicle in a context of important cognitive decline, or if addressed traffic legislation on driving and dementia. Twenty-three articles were selected for full reading, six of which were observational studies and only one with an interventionist method. All articles were carried out in high-income countries such as the UK, the US, and Australia. As a conclusion, there is no psychometric test in the literature sensitive enough to assess vehicle driving competence in older adults with cognitive deficits. Based on selected studies, there is no robust evidence to make recommendation for or against the cessation of vehicular driving for patients with mild cognitive decline or with mild dementia. In some situations, vehicle driving cessation can impact patients and their families. In addition, legal regulations regarding vehicle driving for older adults and people with dementia are scarce worldwide. Despite the scarcity of studies addressing the theme of vehicle driving in the context of dementia, there is some level of consensual reasoning that patients with moderate to severe dementia should halt driving activities, but the same does not apply for patients with mild levels of cognitive impairment, including mild dementia.
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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 | 0.022 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".