The role of vitamin D in chronic heart failure
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite advanced medical and device-based therapies, congestive heart failure (CHF) remains a major medical problem, associated with significant morbidity and mortality. Vitamin D deficiency is prevalent in CHF and is associated with poor outcomes. In this manuscript we review the evidence linking vitamin D deficiency and CHF and discuss potential mechanisms involved, as well the clinical data on vitamin D supplementation in CHF patients. RECENT FINDINGS: A clear relationship has been established between Vitamin D deficiency and increased mortality and morbidity in CHF. However, the mechanism involved is not clearly understood. Recent clinical and experimental evidence have identified the renin-angiotensin-aldosterone system and inflammatory cytokines as likely mediators that can lead to poor clinical outcomes via the cardiorenal syndrome. Clinical data on vitamin D supplementation also remain unestablished, with potential clinical benefits recently reported in patients with vitamin D deficiency. Nonetheless, large-scale randomized clinical trials are lacking. SUMMARY: Vitamin D is an emerging agent with tremendous potential and may represent a novel target for therapy in CHF. Further studies are needed to identify the mechanism(s) involved in the pathophysiology as well as to adequately examine the role of Vitamin D measurement and supplementation in patients with CHF.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".