Driving and Reintegration Into the Community in Patients After Stroke
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To investigate the relationship between driving versus not driving and community integration after stroke. Much research on patients who drive after experiencing a stroke has focused on driving assessment protocols; little attention has been given to the implications of assessment outcomes. DESIGN: Prospective study. SETTING: Six driving evaluation centers in Ontario, Canada. PARTICIPANTS: Fifty-three community-dwelling patients who were referred for a driving assessment after they experienced a stroke. METHODS: Data on demographics, living circumstances, health status, driving habits, and driving history were gathered via a semistructured interview and various questionnaires administered on 3 occasions: study entry (> or =1 month after stroke; n = 53), 3 months (n = 44), and 1 year (n = 43). MAIN OUTCOME MEASUREMENT: Reintegration into the community at 1 year, as evaluated with the Reintegration to Normal Living Index (RNLI). RESULTS: The participants had sustained a stroke an average of 12.3 months before study entry. Two subjects were driving at study entry. At 1 year, 28 (65%) of 43 subjects had passed their driving test and had resumed/continued driving. Nondrivers had a significantly lower mean RNLI score than drivers. Subjects who were not driving at study entry but had resumed driving by 1 year had a significant increase in RNLI score (P = .011). Driving was significantly associated with community integration after adjustment for concomitant health status (P < .001). Driving and health status were associated with community integration at 1 year, accounting for 32% of the variance in RNLI score. CONCLUSIONS: Driving after stroke was significantly associated with community integration in patients after adjustment for health status (P < .001). Community decision-makers may decide to use the study results when determining the transportation needs of stroke survivors who self-limit their driving because of weather, time of day, or distance concerns.
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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 | 0.001 |
| 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 it