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Record W3089293710 · doi:10.14740/jocmr4313

How Can Galectin-3 as a Biomarker of Fibrosis Improve Atrial Fibrillation Diagnosis and Prognosis?

2020· review· en· W3089293710 on OpenAlexvenueno aff
Maria Mariana Barros Melo da Silveira, João Victor Batista Cabral, Bruno de Melo Souza, Louis Hussein Patu Hazime, Sara Larissa de Melo Araújo, Amanda Tavares Xavier, Edivaldo Bezerra Mendes Filho, Luydson Richardson Silva Vasconcelos, Dário Celestino Sobral Filho, Dinaldo Cavalcanti de Oliveira

Bibliographic record

VenueJournal of Clinical Medicine Research · 2020
Typereview
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtrial fibrillationBiomarkerFibrosisGalectin-3Internal medicineCardiologyAblationCardiac fibrosisMyocardial fibrosis

Abstract

fetched live from OpenAlex

Galectin-3 (Gal-3) is a biomarker of fibrosis that has been associated with atrial remodeling. Acknowledging the presence of a biomarker in patients with atrial fibrillation (AF) can allow for a better clinical treatment. The aim of this study was to assess the association of Gal-3 with atrial fibrosis in patients with AF. This is a systematic review study. From the total number of studies analyzed, 12 demonstrated a relation between atrial fibrosis and Gal-3 in patients with AF and presented statistically significant association values. We conclude that Gal-3 is associated with atrial fibrosis in patients with AF in all types, as well as after the arrhythmia treatment by ablation.

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)0.003
metaresearch head score (Gemma)0.013
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 score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.491
GPT teacher head0.583
Teacher spread0.092 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

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