Successfully Navigating to Patient Centered Post Stroke and Post TIA Driving Resources
Bibliographic record
Abstract
Successfully Navigating to Patient Centered Post Stroke & Post TIA Driving Resources Background:Southwestern Ontario is an area with a wide rural geography where driving is essential to everyday living. Following a stroke or Transient Ischemic Attack (TIA), all patients need to be evaluated for their fitness to return to driving. Approximately 50% of those who have had a stroke will return to driving. Understanding and navigating return to driving is confusing and complex. Additionally, those with TIA have difficulty comprehending why they cannot return to driving immediately after their symptoms resolve. Objective:A resource was sought to a) improve patient understanding and navigation for their return to the wheel and b) provide a consistent message for Health Care Providers to utilize when communicating a no-driving message.Methods:An interested group of Occupational Therapists from the Southwestern Ontario Stroke Network (SWOSN) undertook the creation of this resource. This comprehensive iterative process included locating and reviewing provincial and international driving resources from multiple sources as well as consulting legislative documents, experts and professional guidelines. Once a draft was underway patient review and input was sought. Multiple revisions were made to be responsive to all feedback. The document was constructed to be patient centric with headings such as u201cWhat is the process for getting my license back?u201d, u201cWhat happens during a driving assessment?u201d, and u201cWhat if I am no longer able to drive?u201dResults:Two patient centric documents were created; Driving After Stroke in Ontario and Driving After TIA in Ontario. Next Steps:u2022tBroad dissemination across the SWOSN region u2022tStroke survivor and therapist evaluationu2022tIncorporate feedback into future revisions
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".