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Record W4293280727 · doi:10.1093/ckj/sfac189

SGLT-2 inhibitors in nephrotic-range proteinuria: emerging clinical evidence

2022· review· en· W4293280727 on OpenAlexaff
Zeynepgül Kalay, Özgün Ekin Şahin, Sidar Çöpür, Senem Danacı, Alberto Ortíz, Kevin Yau, David Z.I. Cherney, Mehmet Kanbay

Bibliographic record

VenueClinical Kidney Journal · 2022
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersVifor PharmaGlaxoSmithKline AustraliaKyowa Kirin Pharmaceutical DevelopmentIdorsia PharmaceuticalsAmicus TherapeuticsNovo NordiskAstraZenecaChiesi FarmaceuticiMenarini GroupAstellas Pharma USAlexion PharmaceuticalsFresenius Medical Care North AmericaBayerAmgen
KeywordsProteinuriaAlbuminuriaMedicineNephrotic syndromeKidney diseaseRandomized controlled trialInternal medicineGlycemicDiabetes mellitusUrologyIntensive care medicineKidneyEndocrinology

Abstract

fetched live from OpenAlex

Sodium-glucose cotransporter-2 (SGLT-2) inhibitors are a class of novel oral anti-hyperglycemic agents which are increasingly used in clinical practice. SGLT-2 inhibitors improve glycemic control and cardiorenal outcomes, promote weight loss, and reduce blood pressure. Randomized controlled trials have demonstrated that SGLT-2 inhibitors reduce proteinuria and delay progression of kidney disease in patients with albuminuria. However, whether SGLT-2 inhibitors have similar benefits in patients with nephrotic-range proteinuria has not been well established. Evidence to date has been limited to case reports, case series and secondary analyses of randomized controlled trials. This is the first comprehensive review on the effectiveness of SGLT-2 inhibitors for the treatment of patients with nephrotic-range albuminuria or proteinuria. Overall findings support a likely beneficial role of SGLT-2 inhibitors in reducing proteinuria and delaying chronic kidney disease progression in patients with nephrotic-range proteinuria.

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)0.003
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.263
GPT teacher head0.489
Teacher spread0.226 · 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
GenreReview

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

Citations37
Published2022
Admission routes1
Has abstractyes

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