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Record W4200143795 · doi:10.1016/j.eclinm.2021.101240

Nephrotic-range proteinuria in type 2 diabetes: Effects of empagliflozin on kidney disease progression and clinical outcomes

2021· article· en· W4200143795 on OpenAlexaff
Piero Ruggenenti, Bettina J. Kraus, Silvio E. Inzucchi, Bernard Zinman, Stefan Hantel, Michaela Mattheus, Maximilian von Eynatten, Giuseppe Remuzzi, Audrey Koitka‐Weber, Christoph Wanner

Bibliographic record

VenueEClinicalMedicine · 2021
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersDaiichi-SankyoNovo NordiskIntarcia TherapeuticsSanofiAstraZenecaEli Lilly and Company
KeywordsEmpagliflozinMedicineRenal functionKidney diseaseInternal medicineProteinuriaType 2 diabetesPlaceboCreatinineDiabetes mellitusKidneyEndocrinologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: analysis of data from the EMPA-REG OUTCOME trial (NCT01131676). METHODS: Cox proportional hazards models were used to investigate the risk of cardiovascular and kidney outcomes in participants with and without NRP, defined by urine albumin-to-creatinine ratio (UACR) ≥2200 mg/g at baseline. Annual loss of eGFR during chronic treatment (eGFR slopes) and hypothetical time to projected end-stage kidney disease (ESKD), conditioning upon linearity of eGFR change over time if a patient did not decease before projected ESKD, were calculated using a random-intercept random-coefficient model. Safety was described based on investigator-reported adverse events. FINDINGS: 0·005). Empagliflozin was estimated to double the median hypothetical time to projected ESKD in participants with NRP. The overall safety profile of empagliflozin was comparable between participants with and without NRP at baseline. INTERPRETATION: Our data suggests that empagliflozin might slow kidney function loss and delay the estimated onset of projected ESKD in patients with type 2 diabetes and cardiovascular disease complicated by NRP.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.364
Teacher spread0.341 · 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.

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

Citations19
Published2021
Admission routes1
Has abstractyes

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