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Record W4398177417 · doi:10.1016/j.diabres.2024.111715

Impact of empagliflozin on first and recurrent events leading to or prolonging hospitalisation in the EMPA-REG OUTCOME trial

2024· article· en· W4398177417 on OpenAlexaff
Silvio E. Inzucchi, Christoph Wanner, David Fitchett, Bernard Zinman, Stefan D. Anker, Stuart Pocock, Michaela Mattheus, Stefan Hantel, Søren S. Lund

Bibliographic record

VenueDiabetes Research and Clinical Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsMount Sinai HospitalUniversity of TorontoLunenfeld-Tanenbaum Research InstituteSt. Michael's Hospital
FundersAbbott VascularRespicardiaImpulse DynamicsNovo NordiskSanofiServierAmgenPfizerAstraZenecaEli Lilly and Company
KeywordsEmpagliflozinEMPAMedicineDiabetes mellitusOutcome (game theory)Internal medicineType 2 diabetesEndocrinology

Abstract

fetched live from OpenAlex

In EMPA-REG OUTCOME, empagliflozin reduced the composite of total events leading to/prolonging hospitalisation for any cause and all-cause mortality by 24 % versus placebo in patients with T2DM and ASCVD, with 67.7 events prevented/1000 patient-years and a low NNT. Effects were sustained and were consistent regardless of the reason for hospitalisation.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.230
GPT teacher head0.567
Teacher spread0.336 · 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 designNon-randomized 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

Citations0
Published2024
Admission routes1
Has abstractyes

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