European Reference Networks and Guideline Development and Use: Challenges and Opportunities
Bibliographic record
Abstract
BACKGROUND: The Directive 2011/24/EU [OJEU 2011, L88/45] on the application of patient rights in cross-border health care requires the European Commission to support Member States in the development of European reference networks (ERNs). These ERNs are meant to ease the access of patients to highly specialized health care and to facilitate the cooperation at the European Union level in particular medical domains where expertise is scarce, especially in the rare disease area. METHODS: The Directive 2011/24/EU [OJEU 2011, L88/45] and the recent Commission Delegated Decision [OJEU 2014, L147/71] as well as the Implementing Decision [OJEU 2014, L147/79] require ERNs and health care providers wishing to join ERNs to have the capacity of developing good practices guidelines. RESULTS: This provision results in a number of challenges but also opportunities for Member States with respect to guideline production. Member States could consider the importance of devoting resources to build efficient systems and capacities for the development and implementation of trustworthy guidelines. Furthermore, they could adopt a cooperative approach to optimize guideline production across countries. Finally, they could promote the establishment of new research governance based on systematically identified research gaps and prioritized as well as communicated research recommendations. CONCLUSION: Member States are at a decisive point in establishing the details to ensure the transparent and effective functioning of ERNs. Producing explicit plans for the development and use of trustworthy guidelines should be an essential part of this effort.
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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 | 0.454 | 0.555 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.015 | 0.023 |
| Research integrity | 0.024 | 0.023 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".