Abstract T P281: Facilitating Best Practices in Rehabilitation for Persons With Stroke: Use of a Triage Tool in Toronto
Bibliographic record
Abstract
Background: Best practice indicates all stroke patients (including severely affected) benefit from timely and intensive rehabilitation care. Currently in Toronto 27% of patients with stroke are discharged to inpatient rehabilitation from acute care (Canadian Institute for Health Information (CIHI) 11/12). Sixty percent of admissions to rehabilitation were patients with moderate stroke, 17% mild and 22% severe CIHI (FY12-13 Q1-3). Access to rehabilitation in Toronto is not equitable as admission criteria and rehabilitation programming are not standardized for stroke. Purpose: Develop a triage tool to support clinical decision making, equitable access to care and early referral to appropriate rehabilitation based on best practice Methods Acute and rehabilitation leaders collaboratively developed the triage tool. Provincial expert panel recommendations and existing referral frameworks were considered. The AlphaFIM® tool was used as the basis for categorizing stroke severity. Agreement was reached to support ...
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".