Personalized pREvention of Chronic Diseases: the PRECeDI H2020 project
Bibliographic record
Abstract
The aim of the Personalized pREvention of Chronic DIseases consortium (PRECeDI) is to provide high-quality, multidisciplinary knowledge through training and research in Personalized Medicine (PM), with specific reference to prevention of chronic diseases. There is a large consensus that PM is a driver of innovation for research and health care, and also for the health care system and industry as a whole. In order to harness the potential of this new concept, the PRECeDI consortium provides a cohesive framework for training staff from academic and non-academic (NA) institutions on research topics related to PM, with specific reference to the prevention of chronic diseases where there is a lack of substantial evidence, though the potential is huge. The acquisition of skills from staff will come from dedicated secondments aimed at training on research topics not available at the home institutions, and attendance to courses, workshops, seminars, conferences. The goal of secondment is to enable staff to make informed decisions for appropriately serve health care systems, new biotech industries and policy makers at the dawn of the post-genomic era. PRECeDI is a multidisciplinary group of institutions working on different facets of PM, from basic research, to economic evaluations, health service organization, and ethical, social, and policy issues. The consortium is embedded in existing cooperation structures, such as the PerMed project and the Erasmus Mundus ERAWEB II program, with additional leading SMEs in Europe and Canada as beneficiaries. The consortium consists of 11 partners, of which 7 are academic institutions and 4 NA, including 2 SMEs. During 4-years, 30 researchers will be seconded to 11 institutions, where researchers will be supported by a team of leading EU scientists in PM related disciplines. In the long run, PRECeDI will foster the integration of PM in the field of prevention, thus contributing to better health for Europe’s citizen.
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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 | 0.046 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".