European Stroke Organisation (ESO) standard operating procedure for white papers (expert consensus based clinical guidance)
Bibliographic record
Abstract
Promoting the highest quality, evidence-based research across Europe is a priority of the European Stroke Organisation (ESO). The ESO Guideline Board communicate and promote evidence-based recommendations for clinical practice through their Guidelines. However, there are many aspects of stroke care where robust scientific evidence may be unavailable or difficult to obtain. Thus, there is a need for practical, consensus guidance, produced following robust, consistent, and transparent methods, that is suitable for high-priority clinical scenarios where evidence is currently lacking. The ESO Guideline Board developed methods for producing practical clinical guidance based on expert consensus in response to this need. These ESO' White Papers' are intended to complement standard ESO Guidelines. Here, we outline the ESO White Papers' standard operating procedure (SOP). We will describe the motivation for creating White Papers, the preferred composition of writing groups and expert consensus panellists, the methods for achieving consensus, and how results will be communicated. To ensure that all voting members have an equal voice, our methods are based upon the Delphi process of repeated rounds of anonymous voting, feedback and review. We hope that the White Papers will add further value to the clinical practice guidance that is offered by ESO. We look forward to receiving suggestions for White Paper topics from the stroke community.
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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 | 0.350 | 0.603 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.038 | 0.042 |
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".