iCARDIO Alliance Global Implementation Guidelines on Heart Failure 2025
Bibliographic record
Abstract
Inconsistencies in healthcare access, varying infrastructure, resource constraints and diverse local practices as well as practical and political issues restrict the global applicability of currently available guidelines. There is a need for universal recommendations that address the unique challenges faced by patients and healthcare providers worldwide. Our iCARDIO Alliance Global Implementation Guidelines emphasize the incorporation of novel therapies, while integrating standard of care with the most up-to-date evidence to enable clinicians to optimize patient care. This document is about heart failure (HF), including acute and chronic heart failure, heart failure with reduced ejection fraction and heart failure with preserved ejection fraction as well as cardiomyopathies. Context-specific recommendations tailored to individual patient needs are highlighted providing a thorough evaluation of the risks, benefits, and overall value of each therapy, aiming to establish a standard of care that improves patient outcomes and reduces the burden of hospitalization in this susceptible population. These guidelines provide evidence-based recommendations that represent a group consensus considering the many other published guidelines that have reviewed many of the issues discussed here, but they also make new recommendations where new evidence has recently emerged. Most importantly these guidelines also provide recommendations on a number of issues where resource limitations may put constraints on the care provided to HF patients. Such "economic adjustment" recommendations aim to provide guidance for situations when "Resources are somewhat limited" or when "Resources are severely limited". Hence, this document presents not only a comprehensive but also concise update to HF management guidelines thereby aiming to provide a unified strategy for the pharmacological, nonpharmacological, invasive and interventional management of this significant global health challenge that is applicable to the needs of healthcare around the globe.
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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 | 0.039 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.018 | 0.012 |
| Insufficient payload (model declined to judge) | 0.029 | 0.023 |
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".