How to Facilitate Seamless Translation from Basic Concepts to New Heart Failure Drugs. A Scientific Statement of the Heart Failure Association of the ESC
Bibliographic record
Abstract
A rift has opened and is widening between basic research (bench) and clinical research and patients (bed) who need their new treatments, diagnostics and preventive strategies. This problem involving the 'translation' of basic scientific findings into clinical applications and potential treatments or biomarkers for a condition like heart failure is widely recognized both in academia and industry. Despite the attempts that have been made by both sides to improve this situation, the high attrition rates of drug development and the problem with reproducibility and translatability of preclinical findings to human applications still persist. As a result, the return on investment of basic research has been limited in terms of clinical impact. In this scientific statement we describe and discuss various issues with relevance to this theme and try to dissect how to move our field towards the development of more effective heart failure drugs. We zoom in on facilitating the process of heart failure drug development, the unnecessary gaps ('valley of death') between the critical steps in heart failure drug development, validation and de-validation of new concepts as early as possible ('rigorous translation'). We describe forums on how to stimulate cross-talk and interaction between clinician-scientists, basic heart failure researchers, biotech and industry, and how to enable them to speak the same language, and lessons learned from successes outside the heart failure field.
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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 | 0.161 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.020 | 0.018 |
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".