Bibliographic record
Abstract
Funded in the context of the first call of the Marie Curie Research and Innovation Staff Exchange (RISE) 2014 of Horizon 2020, the PREvention of Chronic DIseases consortium (PRECeDI, http://www.precedi.eu/) aims to provide high-quality, multidisciplinary knowledge through training and research in personalized medicine with specific focus on the personalized prevention of chronic diseases. There is a large consensus that personalized medicine is a driver of innovation for research and health care, and also for the health care system and industry as a whole [1]. In order to harness the potential of this new concept, the “PRECeDI” consortium aims to train staff from academic and non-academic institutions on several research topics related to personalized prevention of cancer and neurodegenerative diseases. The acquisition of skills from researchers will come from dedicated secondments aimed at training on different research topics not available at the home institutions; attendance to training courses, workshops, seminars, conferences. In details, five research domains will be addresses: 1) identification and validation of biomarkers for primary prevention of cardiovascular diseases, secondary prevention of Alzheimer, and tertiary prevention of head and neck cancer; 2) economic evaluation of genomic applications; 3) ethical-legal and policy issues surrounding personalized medicine; 4) sociotechnical analysis of the pros-and cons of informing healthy individuals on their genome; 5) identification of organizational models for the provision of predictive genetic testing. PRECeDI is embedded in existing cooperation structures, such as the Erasmus Mundus ERAWEB II program, with additional leading small-medium enterprises (SMEs) in Europe and Canada as beneficiaries. The consortium consists of 9 beneficiaries, namely the Institute of Public Health, Universita del Sacro Cuore, Rome, Italy; Better Value Healthcare Ltd, Oxford, United Kingdom; Department of Infectiuos Diseaseses and Hygiene, Universita La Sapienza, Rome, Italy; Section Community Genetics, VU University Amsterdam, The Netherlands; LINKCARE Health Services S.L., Barcelona, Spain; Erasmus Universitait Medisch Centrum, Department of Epidemiology, Rotterdam, (1) Section of Hygiene, Institute of Public Health, Universita
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; both teacher heads 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".