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Record W1517408528 · doi:10.1159/000435852

European Reference Networks and Guideline Development and Use: Challenges and Opportunities

2015· article· en· W1517408528 on OpenAlexaff
Cristina Morciano, Paola Laricchiuta, Domenica Taruscio, Holger J. Schünemann

Bibliographic record

VenuePublic Health Genomics · 2015
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDirectiveGuidelineEuropean unionBusinessMember statesEuropean commissionHealth careStandardizationCommissionMember stateProcess managementPolitical sciencePublic relationsComputer scienceInternational tradeLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.799
GPT teacher head0.488
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2015
Admission routes1
Has abstractyes

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