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Record W4404110350 · doi:10.1038/s44325-024-00028-4

Consistency of HFrEF treatment effect in underrepresented groups in randomized clinical trials

2024· article· en· W4404110350 on OpenAlexaff
Guillaume Baudry, Luca Monzo, Mark C. Petrie, Nicolas Girerd, Ileana L. Piña, Alexandre Mebazaa, Javed Butler, Leïla Abid, Faı̈ez Zannad, Harriette G.C. Van Spall

Bibliographic record

Venuenpj Cardiovascular Health · 2024
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsRandomized controlled trialConsistency (knowledge bases)Clinical trialMedicineInternal medicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Despite the established efficacy of heart failure (HF) guideline-directed medical therapies, implementation varies across demographic groups, including Black, Indigenous, and people of color, older adults, females, and those who are socioeconomically deprived. This review synthesizes the representativeness of trial participants and describes subgroup analyses from pivotal trials in HF with reduced ejection fraction (HFrEF). It reviews the largely consistent treatment effect of medical therapies across the demographic groups represented in trials. It makes arguments for broad implementation of therapies based on these data, while calling for more representative trials to improve research and health equity in HF.

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)0.037
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0370.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.105
GPT teacher head0.442
Teacher spread0.337 · 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 designRandomized trial
Domainnot available
GenreEmpirical

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

Citations12
Published2024
Admission routes1
Has abstractyes

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