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;
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".