Screening Fitness to Drive After Stroke Across Demographic Subgroups: A Systematic Review
Bibliographic record
Abstract
Return to driving is a valued activity among people who experience stroke. Health care providers, including occupational therapists, require evidence-based tools for driver screening post-stroke, validated for stroke with representation of diverse demographic subgroups. To identify tests supported in the literature predictive of fitness to drive after stroke and critically appraise the representativeness of extant research across demographic subgroups. A systematic literature review was conducted to address the objectives. Consistent with prior research, the Stroke Driver's Screening Assessment and Trail Making Test-B were the most predictive of driver fitness. However, research has consistently underrepresented women, people younger than 55 years of age, and people from low-income countries. Further research is needed with (a) more detailed reporting of participant demographics and (b) increased representation of demographic subgroups within samples, to support culturally informed driver screening practices following stroke.
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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 | 0.017 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".