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Record W4410894074 · doi:10.1016/j.xkme.2025.101040

Aldosterone Breakthrough for the Prediction of Treatment Response to Mineralocorticoid Receptor Antagonists

2025· article· en· W4410894074 on OpenAlexafffund
Ahmed Imcaoudene, Caroline Najjar, Pedro Marques, Michael A. Tsoukas, Abhinav Sharma, Thomas A. Mavrakanas

Bibliographic record

VenueKidney Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsMcGill University Health CentreMcGill University
FundersFonds de Recherche du Québec - SantéNovo NordiskBristol-Myers Squibb CanadaMcGill UniversityServierPfizerAstraZenecaEli Lilly and Company
KeywordsMineralocorticoid receptorAldosteroneMineralocorticoidEndocrinologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) is one of the leading causes of morbidity and mortality.1 Aldosterone is considered one of its key pathophysiological mediators, supporting the use of mineralocorticoid receptor antagonists (MRAs) for the treatment of CKD and concomitant cardiovascular disease.2-4

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.036
GPT teacher head0.326
Teacher spread0.290 · 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 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

Citations2
Published2025
Admission routes2
Has abstractyes

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