How to Integrate Personalized Medicine into Prevention? Recommendations from the Personalized Prevention of Chronic Diseases (PRECeDI) Consortium
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medical practitioners are increasingly adopting a personalized medicine (PM) approach involving individually tailored patient care. The Personalized Prevention of Chronic Diseases (PRECeDI) consortium project, funded within the Marie Skłodowska Curie Action (MSCA) Research and Innovation Staff Exchange (RISE) scheme, had fostered collaboration on PM research and training with special emphasis on the prevention of chronic diseases. From 2014 to 2018, the PRECeDI consortium trained 50 staff members on personalized prevention of chronic diseases through training and research. The acquisition of skills from researchers came from dedicated secondments from academic and nonacademic institutions aimed at training on several research topics related to personalized prevention of cancer and cardiovascular and neurodegenerative diseases. In detail, 5 research domains were addressed: (1) identification and validation of biomarkers for the primary prevention of cardiovascular diseases, secondary prevention of Alzheimer disease, and tertiary prevention of head and neck cancer; (2) economic evaluation of genomic applications; (3) ethical-legal and policy issues surrounding PM; (4) sociotechnical analysis of the pros and cons of informing healthy individuals on their genome; and (5) identification of organizational models for the provision of predictive genetic testing. Based on the results of the research carried out by the PRECeDI consortium, in November 2018, a set of recommendations for policy makers, scientists, and industry has been issued, with the main goal to foster the integration of PM approaches in the field of chronic disease prevention.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it