Effects of Vericiguat in Heart Failure with Reduced Ejection Fraction: Do Not Forget sST2. Reply
Bibliographic record
Abstract
We appreciated the interest of Dr. Aimo and colleagues in our study, and their suggestion to measure soluble suppression of tumorigenesis-2 (sST2) as a potentially useful prognostic marker that might also yield insight into vericiguat's effects in the VICTORIA study.1 While they are correct that no biomarker data, other than natriuretic peptides, were provided in our European Journal of Heart Failure report of the baseline characteristics of VICTORIA patients, we would reassure them that we plan a comprehensive analysis of biomarkers in a pre-specified substudy as outlined previously.2 In that report we outlined an example of eight biomarkers. It is indeed our intention to measure multiple novel biomarkers – including sST2 – in order to provide insight into potential mechanisms of vericiguat's effect.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.023 | 0.031 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".