Bibliographic record
Abstract
BACKGROUND: Although physicians in most provinces are mandated to report patients whose driving ability is impaired by illness, little is known about dementia-related factors associated with driving cessation. The purpose of our study was to explore factors that may affect the likelihood of driving cessation in a sample of elderly, community-dwelling patients with dementia. METHODS: A 3-year prospective study, the Canadian Outcomes Study in Dementia (COSID) has enrolled 883 patients with mild-to-moderate dementia at 32 centres across Canada. Assessment tools included the Mini-Mental State Examination (MMSE) for cognition, the Global Deterioration Scale (GDS) for staging (severity), the Functional Autonomy Measurement System (SMAF) for function, and the Neuropsychiatric Inventory (NPI) for behaviour. Factors associated with the decision to quit driving after the baseline assessment were tested with Cox survival analysis. RESULTS: Of 719 subjects who were or had been drivers, 203 (28.2%) were still driving at baseline. Over an observation period that averaged 23 months, 97 (48.5%) of 200 patients quit driving. Factors predictive of driving cessation included GDS (hazard ratio [HR] 1.68, 95% confidence interval [CI] 1.15-2.45), MMSE score (HR 0.90, 95% CI 0.83-0.97) and NPI findings (HR 1.63 for presence of > or = 3 behaviours, 95% CI 1.01-2.62). Among the NPI behaviours, when they were analyzed separately, agitation led to a decreased likelihood of driving cessation (p = 0.019), whereas apathy (p = 0.031) and hallucinations (p = 0.050) led to an increased likelihood. INTERPRETATION: Cognitive impairment and behaviours such as agitation, apathy and hallucinations were significant predictors of driving cessation in patients with a mild to moderate degree of dementia. These findings should be considered when one counsels patients and their families.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".