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Record W4394873253 · doi:10.1002/ejhf.3240

Time to Clinical Benefit with Sotagliflozin in Patients with Type 2 Diabetes and Chronic Kidney Disease: Insights from the SCORED Randomized Trial

2024· letter· en· W4394873253 on OpenAlexafffundabout
Rahul Aggarwal, Deepak L. Bhatt, Michael Szarek, Lawrence A. Leiter, Christopher P. Cannon, Renato D. Lópes, Michael J. Davies, Phillip Banks, Bertram Pitt, Philippe Gabríel Steg

Bibliographic record

VenueEuropean Journal of Heart Failure · 2024
Typeletter
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersAnschutz Medical Campus, University of ColoradoUniversity of TorontoNational Institutes of HealthBoston Scientific CorporationCleveland ClinicSt. Jude MedicalPfizerDaiichi Sankyo EuropeDuke Clinical Research InstituteSchool of Medicine, Duke UniversityBristol-Myers Squibb
KeywordsMedicineRandomized controlled trialKidney diseaseDiabetes mellitusClinical trialType 2 diabetesDiseaseHeart failureIntensive care medicineInternal medicineMEDLINEEndocrinology

Abstract

fetched live from OpenAlex

Peer Reviewed

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)0.005
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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 designRandomized trial
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

Citations4
Published2024
Admission routes3
Has abstractyes

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