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 automatic acceptance of patients referred to rehab with AlphaFIM® score of 60-80. The tool was implemented Feb 1, 2013. All rehabilitation organizations agreed to standardized admission criteria and are working toward best practice for stroke inpatient rehabilitation care. Results: Baseline data (January to August 2012) for patients referred with an early AlphaFIM® of 60-80 indicated only 66% were accepted to rehab, 15% were declined and 8.5% had a decision pending. For those declined, 10% were identified as having special needs that could not be met, 29% were considered more appropriate for slow stream rehab, and 14% because of limited sitting tolerance and balance. An analysis of data following implementation will be presented. Conclusion: The triage tool creates a standard of best practice for the system. Agreement on common admission criteria and standard of practice for rehabilitation referral management for patients with AlphaFIM® 60-80 have been established between referrers and rehab providers. It is expected that transition barriers for this group should be minimal unless special needs are identified.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".