Differences between poststroke drivers and nondrivers: demographic characteristics, medical status, and transportation use.
Bibliographic record
Abstract
OBJECTIVE: To determine the demographic, medical, and transportation use characteristics of stroke survivors wanting to drive who resumed or did not resume driving and compare the driving habits of those who drove with those of a nonstroke control group. DESIGN: One hundred and six stroke survivors who underwent a driving evaluation at a rehabilitation center in Ottawa, Canada, between 1995 and 2003, participated in a structured telephone interview 4-5 yrs after the evaluation. Information on driving history and transportation use before the driving assessment was obtained from the driving assessment client database. The nonstroke control group was derived from the literature. RESULTS: After stroke, 66% of subjects had resumed driving. Prestroke driving history was similar for drivers and nondrivers. Drivers were younger than nondrivers (mean age +/- SD, 62.7 +/- 12.7 yrs vs. 69.2 +/- 13.4 yrs; P = 0.02), had less medical comorbidity (mean modified Cumulative Illness Rating Scale score, 3.7 +/- 1.97 vs. 5.0 +/- 2.89; P = 0.01), and were less likely to rely on a walker (1.4% vs. 19.4%, P < 0.001). Self-imposed restrictions were reported by 35.7% of drivers. More nondrivers than drivers relied on family/friends (94.4% vs. 41.4%), public transportation (60.7% vs. 35.3%), or taxis (27.8% vs. 2.9%) (all P < 0.05). Drivers reported fewer driving difficulties (e.g., skill, weather, or traffic related; <or=20% for each) than the nonstroke group. Five of 12 licensed patients with stroke who drove to their first assessment failed it. CONCLUSIONS: In a sample of stroke survivors who had similar driving histories before their stroke and who were deemed to have the potential to drive, those who resumed driving after their stroke were younger, had fewer medical problems, and were less disabled than those who did not return to driving. Self-imposed driving restrictions were common. Compared with drivers, nondrivers relied more on friends, family, public transportation, and taxis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".