Early in-Hospital Treatment of Acute Heart Failure. Part 2 of the International Expert Opinion Series on AHF Management
Bibliographic record
Abstract
Acute heart failure (AHF) remains a major global health challenge, contributing significantly to morbidity, mortality and healthcare resource utilization. It is one of the leading causes of hospitalization, with persistently high readmission rates underscoring the need for improved early management strategies. Despite its prevalence, clear and evidence-based guidance for the early evaluation and treatment of AHF is limited. Congestion is the primary reason for emergency admission, making rapid and effective decongestion a top priority, but diuretics are often underdosed in AHF patients. Medications proven to improve mortality are often not started. In this state-of-the-art review, we address this critical gap by outlining a practical, evidence-based framework for the early management of AHF. Key components include early identification of co-existing conditions, bedside haemodynamic profiling, a structured diagnostic approach incorporating both standard and individualized assessments, a stepwise pharmacologic diuretic strategy beginning with high-dose intravenous loop diuretics, and early in-hospital initiation of guideline-directed medical therapy.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".