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Record W4253320120 · doi:10.1056/nejmc1917241

Dapagliflozin in Patients with Heart Failure and Reduced Ejection Fraction

2020· letter· en· W4253320120 on OpenAlexfundno aff

Bibliographic record

VenueNew England Journal of Medicine · 2020
Typeletter
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
FundersCumming School of Medicine, University of CalgaryTaipei Veterans General HospitalUniwersytet Medyczny im. Piastów Slaskich we WroclawiuUppsala UniversitetUniversidad de CórdobaNational Yang-Ming UniversityInstitut de Cardiologie de MontréalUniversität des SaarlandesSemmelweis EgyetemUniversity of TorontoNational Cerebral and Cardiovascular CenterUniwersytet ŁódzkiUniversidad Nacional de CórdobaUniverzita Komenského v BratislaveRijksuniversiteit GroningenUniverzita Karlova v PrazeFudan UniversityUniversity of MissouriUniversity of MinnesotaSahlgrenska AkademinAstraZeneca
KeywordsDapagliflozinEjection fractionHeart failureHazard ratioMedicinePlaceboInternal medicineConfidence intervalCardiologyDiabetes mellitusType 2 diabetesEndocrinology

Abstract

fetched live from OpenAlex

In the DAPA-HF (Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure) trial reported by McMurray et al. (Nov. 21 issue),1 among patients with heart failure and a reduced ejection fraction, the risk of worsening heart failure or death from cardiovascular causes was significantly lower among those who received dapagliflozin than among those who received placebo, regardless of the presence or absence of diabetes. These data present a new perspective for the care of patients with heart failure. However, the trial did not address the potential role of a change in uric acid levels as a mediator of the improvement in cardiovascular outcomes in the enrolled patients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0040.004

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.

Opus teacher head0.010
GPT teacher head0.229
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations61
Published2020
Admission routes1
Has abstractyes

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