Clinical Profiles in Acute Heart Failure: An Urgent Need for a New Approach
Bibliographic record
Abstract
Acute heart failure (HF) is a major public health concern, responsible for >26 million hospitalizations per year worldwide. Many trials have investigated new therapeutic options for acute HF, with most revealing equivocal results. Successful innovations in therapy for acute HF have remained limited, and standard of care has remained largely unchanged over the past decade, suggesting the need for a new approach for therapeutic decision making and clinical trial design in acute HF. This manuscript focuses on one approach that could prove useful in the development and application of novel therapies: classification of patients based on clinical profiles. While previous attempts at developing clinical profiles were successful in stratifying patients based on clinical and laboratory variables, they have not been utilized for personalized treatment strategies that improve patient outcomes. We suggest a new approach to the creation of clinical profiles that could stratify patients based on their underlying aetiology and their response to novel interventions. We also investigate novel analytic approaches to the creation of new clinical profiles that both investigators and clinicians alike could utilize to inform clinical trial design and the application of new therapies. Despite a large number of clinical trials for new therapeutic options, the treatment of acute HF has seen few advances over the past decades. Innovative approaches to patient selection through the use of clinical profiles could help to identify patients most likely to benefit from novel interventions and lead to the discovery of new therapeutic options.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".