Rationale and Design of the ESC Heart Failure III Registry – Implementation and Discovery
Bibliographic record
Abstract
AIMS: Heart failure outcomes remain poor despite advances in therapy. The European Society of Cardiology Heart Failure III Registry (ESC HF III Registry) aims to characterize HF clinical features and outcomes and to assess implementation of guideline-recommended therapy in Europe and other ESC affiliated countries. METHODS: Between 1 November 2018 and 31 December 2020, 10 162 patients with chronic or acute/worsening HF with reduced, mildly reduced, or preserved ejection fraction were enrolled from 220 centres in 41 European or ESC affiliated countries. The ESC HF III Registry collected data on baseline characteristics (hospital or clinic presentation), hospital course, diagnostic and therapeutic decisions in hospital and at the clinic visit; and on outcomes at 12-month follow-up. These data include demographics, medical history, physical examination, biomarkers and imaging, quality of life, treatments, and interventions - including drug doses and reasons for non-use, and cause-specific outcomes. CONCLUSION: The ESC HF III Registry will provide comprehensive and unique insight into contemporary HF characteristics, treatment implementation, and outcomes, and may impact implementation strategies, clinical discovery, trial design, and public policy.
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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 | 0.265 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".