Criteria and Indicators for Centers of Clinical Excellence in Stroke Recovery and Rehabilitation: A Global Consensus Facilitated by ISRRA
Bibliographic record
Abstract
BACKGROUND: The aim of the International Stroke Recovery and Rehabilitation Alliance is to create a world where worldwide collaboration brings major breakthroughs for the millions of people living with stroke. A key pillar of this work is to define globally relevant criteria for centers that aspire to deliver excellent clinical rehabilitation and generate exceptional outcomes for patients. OBJECTIVES: This paper presents consensus work conducted with an international group of expert stroke recovery and rehabilitation researchers, clinicians, and people living with stroke to identify and define criteria and measurable indicators for Centers of Clinical Excellence (CoCE) in stroke recovery and rehabilitation. These were intentionally developed to be ambitious and internationally relevant, regardless of a country's development or income status, to drive global improvement in stroke services. METHODS: Criteria and specific measurable indicators for CoCE were collaboratively developed by an international panel of stroke recovery and rehabilitation experts from 10 countries and consumer groups from 5 countries. RESULTS: The criteria and associated indicators, ranked in order of importance, focused upon (i) optimal outcome, (ii) research culture, (iii) working collaboratively with people living with stroke, (iv) knowledge exchange, (v) leadership, (vi) education, and (vii) advocacy. Work is currently underway to user-test the criteria and indicators in 14 rehabilitation centers in 10 different countries. CONCLUSIONS: We anticipate that use of the criteria and indicators could support individual organizations to further develop their services and, more widely, provide a mechanism by which clinical excellence can be articulated and shared to generate global improvements in stroke care.
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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 | 0.320 | 0.223 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.012 | 0.028 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".