Standardized Definitions for Evaluation of Acute Decompensated Heart Failure Therapies
Bibliographic record
Abstract
Acute decompensated heart failure (ADHF) is one of the most common reasons for hospitalizations or urgent care and is associated with poor outcomes. Therapies shown to improve outcomes are limited, however, and innovation in pharmacologic and device-based therapeutics are therefore actively being sought. Standardizing definitions for ADHF and its trajectory is complex, limiting the generalizability and translation of clinical trials to effect clinical care and policy change. The Heart Failure Collaboratory is a multistakeholder organization comprising clinical investigators, clinicians, patients, government representatives (including U.S. Food and Drug Administration and National Institutes of Health participants), payors, and industry collaborators. The following expert consensus document is the product of the Heart Failure Collaboratory convening with the Academic Research Consortium, including members from academia, the U.S. Food and Drug Administration, and industry, for the purposes of proposing standardized definitions for ADHF and highlighting important endpoint considerations to inform the design and conduct of clinical trials for drugs and devices in this clinical arena.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".