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Mineralocorticoid receptor antagonists in patients with chronic kidney disease

2020· review· en· W3090601183 on OpenAlexaff
Cosimo Cosimato, Thomas Agoritsas, Thomas A. Mavrakanas

Bibliographic record

VenuePharmacology & Therapeutics · 2020
Typereview
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsMcGill UniversityImpactMcMaster University
FundersUniversité de Genève
KeywordsMedicineKidney diseaseMineralocorticoid receptorHyperkalemiaIntensive care medicineEplerenonePopulationInternal medicineHeart failureRandomized controlled trialDiseaseDialysisClinical trialAldosterone

Abstract

fetched live from OpenAlex

Mineralocorticoid receptor antagonists (MRA) can reduce cardiovascular morbidity and mortality in patients with heart failure and ischemic heart disease. In addition, these agents have been used in patients with diabetic nephropathy to control proteinuria and slow down chronic kidney disease (CKD) progression. Current guidelines recommend against the use of MRAs in patients with advanced CKD. However, there is growing interest on their use in this population that has unmet needs (high cardiovascular morbidity and mortality) and unique challenges (risk of acute kidney injury or hyperkalemia). This narrative review discusses the emerging role of MRAs for the management of cardiovascular disease and/or the prevention of CKD progression, highlighting results from randomized controlled trials and presenting real-world data from available registries. Results from recent trials in patients on maintenance dialysis are also discussed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.348
Teacher spread0.304 · 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.

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

Citations32
Published2020
Admission routes1
Has abstractyes

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