Understanding driving after stroke: safety outcomes and characteristics of those who return to driving
Bibliographic record
Abstract
It is difficult to assess driving safety according to stroke sequelae, given that little attention has been given to the characteristics of post-stroke drivers.Therefore, the specific objective was to estimate the extent to which neurological and functional factors at 3 months predict driving resumption at 12 months poststroke.A sub-cohort of drivers (n=290) were sampled from 678 Canadians who were participating in a longitudinal study of stroke outcomes.Of 290 participants (68% men; age: 64 years; CNS: 8.5), 177 (61%) returned to driving at one year.The relationships between the factors influencing driving status were modeled in a path analysis.Persons with higher scores on the combined SIS scales for strength and activity and MMSE, as well as those with ischemic stroke, will be more likely to return to driving.Fatigue, gender and stroke severity indirectly influence driving through strength and activity.Gender differences were also found in the level of fatigue.Future work that carefully incorporates more sensitive measures of function in a path model with contextual factors will confirm these results.ii
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".