Beyond 1 Million Genomes (B1MG) D5.3 Economic models methodology and case studies
Bibliographic record
Abstract
Please note that the scope of this Deliverable has been extended following feedback from the reviewers and the title “Health Economics Models for Genomics in Healthcare - Recommendations for the application of Health Technology Assessment and Health Economics to genomics in the framework of the 1+MG Initiative” best describes the Deliverable after the introduced changes. The B1MG Work Package 5 (WP5) has among its tasks one dedicated to address health economics aspects of the adoption of genomics in health-care. As a result of the activities performed by the WP5 is this deliverable that tries to contribute to the discussions about a sustainable implementation of genomics in the European countries providing recommendations about Health Technology Assessment and Health Economics and their application to genomics. It is necessary to clarify that the HTA concept includes Health Economics as one of the essential domains to be evaluated. In order to elaborate this document the WP5 has organised a workshop entitled “Health Technology Assessment and Health Economics of Genomics in Health-care: Key Issues for Implementation”. The workshop was organised with the objective of providing insights and facilitating discussion on experiences of national genome initiatives and/or relevant projects from Canada, France, the Netherlands and the United Kingdom. A total of 218 participants from 40 different countries were registered, mainly from academia, governmental organisations and industry. Researchers from ISCIII, Lygature and INSA coordinated the elaboration of this deliverable that is based on the experiences shared and lessons learned in the workshop and during the preparation of the workshop. This activity has been useful to identify some key challenges for a successful application of the HTA methods to genomics. In addition the activity has served to develop a series of key recommendations, some related to the role of HTA in genomics, others with methodology and others about opportunities for international collaboration.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".