Acute Heart Failure: Lessons Learned, Roads Ahead
Bibliographic record
Abstract
Acute heart failure remains a major challenge for clinicians and healthcare systems.The number of annual hospitalizations for acute heart failure is rising due to the aging of the general population and the increasing prevalence of heart failure.Heart failure is the leading cause of unplanned hospitalizations for patients older than 65 years in developed countries.1 -4 These acute events impact the natural history of heart failure progression, as demonstrated by the dramatic increase in the rate of death and rehospitalizations after an acute heart failure episode.5 -7 Similarly, unplanned visits for worsening symptoms requiring intravenous diuretic treatment are also associated with poor prognosis, with a greater than four-fold increase in subsequent mortality.8,9 The available treatment options (primarily diuretics or vasodilators in normo/hypertensive patients) provide symptomatic relief, 1,10 but no therapies for acute heart failure have been shown to improve clinical outcomes in prospective, randomized trials.Thus, reducing morbidity and prolonging survival remain major unmet needs for patients with acute heart failure.10 -12 Acute heart failure is an ideal target for development of new therapeutic interventions given its high frequency and negative impact on clinical outcomes.However, substantial investments in research and development have not yielded proof of efficacy and safety for any of the therapies tested. Results of recent mega-trials in acute heart failureThe goal of improving outcomes for patients with acute heart failure has fostered an emphasis on mega-trials, designed to enrol a sufficiently large number of patients to detect improvements in
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".