Integrating supplementation in the management of patients with heart failure: an evidence-based review
Bibliographic record
Abstract
INTRODUCTION: Complementary, alternative and integrative medicine includes a myriad of therapies including herbal medicines, vitamins, dietary interventions and more, that are taken alone or in adjunct to standard conventional treatment. Often the main goals are to slow progression of disease, increase effectiveness of a drug, reduce side effects and improve quality of life. The study of these therapies and their influence in heart failure is not new. However, even for an experienced clinician, a gap exists between the literature and the application of knowledge to make a confident recommendation. AREAS COVERED: This review has a focus on specific supplements that are commonly used for individuals with HF. It discusses the mechanism of action, expected benefits, potential adverse effects, suggested doses, forms and drug interactions of these therapies. The literature search methodology included using medical subject headings terms to search in PubMed. Articles used were screened and critically appraised by the authors of this review. EXPERT OPINION: There are promising outcomes pertaining to the use of CAM in patients with HF. Advances in large scale, randomized, placebo-controlled trials are necessary to support evidence-based decision making regarding the use of supplements in conjunction, and in comparison, to conventional therapies for heart failure.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".