DARWIN EU® is an electronic platform for the collection and analysis of health data in the European Union
Bibliographic record
Abstract
The growing volume and complexity of data that is currently being collected in a variety of settings and devices is of varying degrees and quality, posing a challenge for the public health sector to develop a robust electronic system that can be collected, analyzed and made available to clinicians. The creation of a system, the development of the capabilities and potential of information technologies for obtaining, managing and analyzing a large amount of data, will allow to identify facts regarding the safety and effectiveness of the use of drugs, to investigate the validity of statements made by pharmaceutical companies, to obtain a more accurate characterization of treatment methods in individual healthcare sectors and to provide doctors with quick and constant access to this information, which will facilitate its use in the treatment of patients. The development of such a tool is a priority for the health care system in a changing world. An example of such a tool is the Data Analysis and Real Word Interrogation Network (DARWIN EU®), which was launched on February 9, 2022 by the European Medicines Agency. The purpose of this article is to review the history of creation, organizational structure, operating principles, current experience of the European Union regulatory network and comparison with the experience of international regulatory bodies. The article, along with the experience of the European Medicines Agency, also considers similar initiatives in the US and Canada.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 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".