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Record W3007515386 · doi:10.1002/ejhf.1765

Effects of Vericiguat in Heart Failure with Reduced Ejection Fraction: Do Not Forget sST2. Reply

2020· letter· en· W3007515386 on OpenAlexaff
Paul W. Armstrong, Burkert Pieske, Christopher M. O’Connor

Bibliographic record

VenueEuropean Journal of Heart Failure · 2020
Typeletter
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsCanadian VIGOUR CentreUniversity of Alberta
Fundersnot available
KeywordsMedicineHeart failureEjection fractionBiomarkerInternal medicineCardiologyNatriuretic peptideBaseline (sea)Measure (data warehouse)Data mining

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.206
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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