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Record W4402640848 · doi:10.1007/s12325-024-02961-3

Summary of Research: Dapagliflozin Utilization in Chronic Kidney Disease and Its Real-World Effectiveness Among Patients with Lower Levels of Albuminuria in the USA and Japan

2024· article· en· W4402640848 on OpenAlexaff
Navdeep Tangri, Anjay Rastogi, Tadashi Sofue

Bibliographic record

VenueAdvances in Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSeven Oaks General Hospital
FundersAstraZeneca
KeywordsMedicineAlbuminuriaDapagliflozinKidney diseaseInternal medicineIntensive care medicineDiseaseUrologyEndocrinologyDiabetes mellitusType 2 diabetes

Abstract

fetched live from OpenAlex

This is a summary of the original article 'Dapagliflozin Utilization in Chronic Kidney Disease and Its Real-World Effectiveness Among Patients with Lower Levels of Albuminuria in the USA and Japan'. The slowing down of kidney function decline is important for managing chronic kidney disease (CKD) and preventing its complications. Clinical trials of dapagliflozin, a sodium-glucose cotransporter-2 inhibitor (SGLT-2i), have shown reductions in disease progression and death in patients with CKD and elevated levels of albuminuria. This summary of research provides an overview of a previously published article that aimed to find out whether dapagliflozin is also effective in patients with lower levels of albuminuria [urinary albumin-to-creatinine ratio (UACR) below 200 mg/g]. Starting dapagliflozin was associated with slower kidney function decline in patients with CKD and UACR below 200 mg/g compared with not starting. This effect was also observed in a subgroup analysis of patients without type 2 diabetes. These results suggest that the established benefits of SGLT-2is may extend to patients with lower levels of albuminuria.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.360
Teacher spread0.318 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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