Building a European ‘network of networks’ for stroke clinical research – The European Stroke Organisation Trials Alliance (ESOTA)
Bibliographic record
Abstract
Promoting research to improve stroke prevention, acute care, and recovery is a key mission of the European Stroke Organisation (ESO). Stroke research networks may increase efficiencies and reduce waste in randomised clinical trials of stroke treatments. Several European countries have established national or regional stroke research networks, or have informal groups or stroke registers which may serve as a foundation for establishing a research network. To increase international collaboration on randomised trials for stroke in Europe, the ESO Trials Network Committee is leading the development of an alliance of national networks, the ESO Trials Alliance (ESOTA). Following initial consultation work in 2017, this paper describes an overview of progress to date in the first year of ESOTA activity. Beginning with five founding networks in England, Ireland, Netherlands, Spain, and Switzerland, ESOTA aims to gradually grow, ultimately including several hundred stroke centres and affiliated investigators working collectively on randomised trials of new stroke treatments.
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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 | 0.312 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".