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Record W4366832628 · doi:10.1051/medsci/2023008

Antagonistes du récepteur minéralocorticoïde

2023· article· fr· W4366832628 on OpenAlexaff
Sophie Girerd, Matthieu Soulié, Jonatan Barrera‐Chimal, Frédéric Jaisser

Bibliographic record

Venuemédecine/sciences · 2023
Typearticle
Languagefr
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsHôpital Maisonneuve-Rosemont
FundersAgence Nationale de la Recherche
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

La maladie rénale diabétique (MRD) et ses comorbidités cardiovasculaires représentent des complications majeures chez les patients diabétiques. Au cours des deux dernières décennies, plusieurs études expérimentales ont montré le bénéfice cardiorénal apporté par les antagonistes du récepteur minéralocorticoïde (RM) dans des modèles animaux de maladies rénales diabétiques ou non. Dans cette synthèse, nous présentons le rôle de l’activation du RM dans l’induction des mécanismes inflammatoires et fibrosants qui contribuent à la physiopathologie de la MRD. Nous passons également en revue les principales conclusions de deux grands essais cliniques récents, FIDELIO-DKD et FIGARO-DKD, qui ont montré pour la première fois un bénéfice majeur de l’antagoniste non stéroïdien du RM, la finerénone, pour la réduction des risques rénaux et cardiaques chez les patients présentant une MRD. Nous discutons enfin de la place de la finerénone par rapport aux autres approches thérapeutiques actuelles et futures de la MRD.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.064
GPT teacher head0.327
Teacher spread0.264 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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