A stroke recovery trial development framework: Consensus-based core recommendations from the Second Stroke Recovery and Rehabilitation Roundtable
Bibliographic record
Abstract
A major goal of the Stroke Recovery and Rehabilitation Roundtable (SRRR) is to accelerate development of effective treatments to enhance stroke recovery beyond that expected to occur spontaneously or with current approaches. In this paper, we describe key issues for the next generation of stroke recovery treatment trials and present the Stroke Recovery and Rehabilitation Roundtable Trials Development Framework (SRRR-TDF). An exemplar (an upper limb recovery trial) is presented to demonstrate the utility of this framework to guide the GO, NO-GO decision-making process in trial development.
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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 | 0.735 | 0.684 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.026 | 0.025 |
| Research integrity | 0.039 | 0.043 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".