Setting the scene for the Second Stroke Recovery and Rehabilitation Roundtable
Bibliographic record
Abstract
The Stroke Recovery and Rehabilitation Roundtable (SRRR) meetings bring together an international group of preclinical and clinical researchers along with statisticians, methodologists, funders and consumers, working to accelerate the development of effective treatments for stroke recovery and to support best-evidence uptake in rehabilitation practice. The first meeting (2016) focused on four recommendation areas: translation of preclinical evidence into human discovery trials; recovery biomarkers to provide knowledge of therapeutic targets and prognosis in human stroke; intervention development, monitoring, and reporting standards; and standardized measurement in motor recovery trials. The impact of SRRR is growing, with uptake of recommendations emerging, and funders exploring ways to incorporate research targets and recommendations. At our second meeting (SRRR2, 2018), we worked on new priority areas: (1) cognitive impairment, (2) standardizing metrics for measuring quality of movement, (3) improving development of recovery trials, and (4) moving evidence-based treatments into practice. To accelerate progress towards breakthrough treatments, formation of an International Stroke Recovery and Rehabilitation Alliance is our next step, where working groups will take recommendations and build partnerships needed to achieve our goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.070 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.035 | 0.041 |
| Insufficient payload (model declined to judge) | 0.074 | 0.023 |
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".