EUreccA 2025: A Multistakeholder Effort to Further Real-World Evidence in Healthcare Decision Making
Bibliographic record
Abstract
The maturation of use of real-world evidence (RWE) in decision making requires substantiation and growth in several areas including data sources and catalogs, data quality, governance, analytic methods, reporting standards, transparency, replicability, and fit-for-purpose specifications.1-3 During the past few years, recent guidance documents from the US Food and Drug Administration, European Medicines Agency, and many other agencies and societies have begun to address these areas.4-6 Health technology assessment (HTA) agencies or coalitions such as the National Institute for Health and Care Excellence in the United Kingdom, European Network for Health Technology Assessment, Real-World Data in Asia for Health Technology Assessment in Reimbursement, and Canadian Agency for Drugs and Technologies in Health have also issued guidance.
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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 | 0.286 | 0.323 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.015 | 0.023 |
| Research integrity | 0.034 | 0.022 |
| Insufficient payload (model declined to judge) | 0.054 | 0.026 |
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".