Meta-analysis of Driving Cessation and Dementia: Does Sex Matter?
Bibliographic record
Abstract
Objectives: The number of drivers with dementia is expected to increase over the coming decades. Because dementia is associated with a higher risk of crashes, driving cessation becomes inevitable as the disease progresses, but many people with dementia resist stopping to drive. This meta-analysis examines whether there are sex differences in the prevalence and incidence of driving cessation among drivers with dementia and compares the pattern of sex differences in drivers with dementia to those without dementia. Method: MEDLINE, PsycINFO, Scopus, and CINAHL were searched in July 2015 for observational studies of sex differences in driving cessation. Meta-analyses were performed using a random-effects model. Results: Twenty studies provided data on sex differences in driving cessation in older adults with or without dementia. Driving cessation was significantly more prevalent in women with dementia than men (odds ratio [OR] = 2.11, 95% confidence interval [CI] = 1.50-2.98), and the same pattern was found in women without dementia (OR = 2.74, 95% CI = 1.85-4.06). Discussion: Our findings suggest that the patterns of driving cessation differ between men and women with dementia, and this may have implications for sex-specific approaches designed to support drivers with dementia both before and after driving cessation.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.039 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".