Systems Medicine Approaches for the Definition of Complex Phenotypes in Chronic Diseases and Ageing. From Concept to Implementation and Policies
Bibliographic record
Abstract
Chronic diseases are diseases of long duration and slow progression. Major NCDs (cardiovascular diseases, cancer, chronic respiratory diseases, diabetes, rheumatologic diseases and mental health) represent the predominant health problem of the Century. The prevention and control of NCDs are the priority of the World Health Organization 2008 Action Plan, the United Nations 2010 Resolution and the European Union 2010 Council. The novel trend for the management of NCDs is evolving towards integrative, holistic approaches. NCDs are intertwined with ageing. The European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) has prioritised NCDs. To tackle them in their totality in order to reduce their burden and societal impact, it is proposed that NCDs should be considered as a single expression of disease with different risk factors and entities. An innovative integrated health system built around systems medicine and strategic partnerships is proposed to combat NCDs. It includes (i) understanding the social, economic, environmental, genetic determinants, as well as the molecular and cellular mechanisms underlying NCDs; (ii) primary care and practice-based interprofessional collaboration; (iii) carefully phenotyped patients; (iv) development of unbiased and accurate biomarkers for comorbidities, severity and follow up of patients; (v) socio-economic science; (vi) development of guidelines; (vii) training; and (viii) policy decisions. The results could be applicable to all countries and adapted to local needs, economy and health systems. This paper reviews the complexity of NCDs intertwined with ageing. It gives an overview of the problem and proposes two practical examples of systems medicine (MeDALL) applied to allergy and to NCD co-morbidities (MACVIA-LR, Reference Site of the European Innovation Partnership on Active and Healthy Ageing).
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 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 | 0.034 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".