Implementing the SRRR taskforce recommendations to transform stroke recovery research
Bibliographic record
Abstract
Implementing the SRRR taskforce recommendations to transform stroke recovery researchStroke is the second biggest killer worldwide in people over 60 and disproportionately affects people in resource-poor countries.Research implementation over the past 25 years has successfully led to a number of acute treatments that have dramatically improved survival and outcomes for people with stroke.High-quality research and consensus in methodological approaches to stroke recovery and rehabilitation research is now more vital than ever, to deliver successful evidence-based interventions that will enable an increasing number of stroke survivors to have a better life after stroke.We know that many stroke survivors are not getting the post-acute care and support that they need, and a key part of our role to ensure this happens is to fund robust, game-changing research for implementation in healthcare policy and practice to support survivors' recovery and help them rebuild their lives after stroke.As funders, we have a responsibility to spend our publicly donated funds very carefully, on highquality research that leads to impact for patients.
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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 | 0.052 | 0.144 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.048 | 0.050 |
| Insufficient payload (model declined to judge) | 0.023 | 0.024 |
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".