Inter‐relationships between cardiovascular, renal and metabolic diseases: Underlying evidence and implications for integrated interdisciplinary care and management
Bibliographic record
Abstract
Cardiovascular, renal and metabolic (CaReMe) diseases are individually among the leading global causes of death, and each is associated with substantial morbidity and mortality. However, as these conditions commonly coexist in the same patient, the individual risk of mortality and morbidity is further compounded, leading to a considerable healthcare burden. A number of pathophysiological pathways are common to diseases of the CaReMe spectrum, including neurohormonal dysfunction, visceral adiposity and insulin resistance, oxidative stress and systemic inflammation. Because of the shared pathology and common co-occurrence of the CaReMe diseases, the value of managing these conditions holistically is increasingly being realized. A number of pharmacological and non-pharmacological approaches have been shown to offer simultaneous metabolic, cardioprotective and renoprotective benefits, leading to improved patient outcomes across the CaReMe spectrum. In addition, increasing value is being placed on interdisciplinary team-based and coordinated care models built on greater integration between specialties to increase the rate of early diagnosis and adherence to practice guidelines, and improve clinical outcomes. This interdisciplinary approach also facilitates integration between primary and specialty care, improving the patient experience, optimizing resources, and leading to efficiencies and cost savings. As the burden of CaReMe diseases continues to increase, implementation of innovative and integrated care delivery models will be essential to achieve effective and efficient chronic disease management and to ensure that patients benefit from the best care available across all three disciplines.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".