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Record W4415687071 · doi:10.1080/23995270.2025.2570636

Plain Language Summary: kidney function and severe clinical events in people with Fabry disease who receive agalsidase beta

2025· article· en· W4415687071 on OpenAlexaff
Julie L. Batista, Jerry Walter, Elvira Ponce, Christoph Wanner, Robert J. Desnick

Bibliographic record

VenueFuture Rare Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsGlobal Affairs Canada
FundersSanofi
KeywordsFabry diseaseRenal functionBETA (programming language)Kidney diseaseDisease

Abstract

fetched live from OpenAlex

Plain Language SummaryWhat did this study look at?In Fabry disease an enzyme (a protein that speeds up chemical reactions in the body) called alpha-galactosidase A, does not work properly or is absent. This causes a fat called globotriaosylceramide to build up inside cells, leading to blood vessels, kidney, and heart problems. People with Fabry disease face higher risks of serious health problems (e.g., needing dialysis [a treatment that filters waste from the blood when kidneys fail] or having a stroke) or may die younger. Agalsidase beta is an enzyme replacement therapy for the treatment of Fabry disease. This study investigated the long-term treatment effect of agalsidase beta, on improving kidney function (how the kidneys clean blood and control body fluids) and reducing clinical events, by comparing real-world data from people with Fabry disease who received agalsidase beta to untreated individuals. People were matched 1:1 (treated: untreated) based on their characteristics, age and kidney function.What were the results of the study?This study found that a decline in kidney function over 5 years was slower in people with Fabry disease receiving agalsidase beta compared with those who were untreated. Furthermore, those receiving agalsidase beta had a lower risk of clinical events over time than those who were untreated.What do these results mean?Patients treated with agalsidase beta had better kidney function and health outcomes than untreated patients. These real-world findings highlight agalsidase beta’s effectiveness as a therapy for Fabry disease.How to say (download PDF and double click sound icon to play sound)…Agalsidase beta: ay-GAL-sih-daze BAY-tahAlpha-galactosidase A (also called alpha-Gal A): AL-fa gal-ACT-oh-side-aze AYAtrial fibrillation: AY-tree-ul fih-bruh-LAY-shunCardiovascular: KAR-dee-oh-VAS-kyoo-larCerebrovascular: SUR-ree-broh- VAS-kyoo-larFabry: fab-REE or fab-RAYGlobotriaosylceramide (also called GL-3 or Gb3): gloh-boh-try-AY-sill-SER-ah-mideGlobotriaosylsphingosine (also called lyso-GL-3 or lyso-Gb3): gloh-boh-try-AY-sill-SFING-goh-seenGlomerular: glom-AIR-yool-areHaemorrhagic: HEH-muh-RA-juhkIschaemic: is-KEE-mikVentricular tachycardia: ven-TRIH-kyoo-ler TA-kih-KAR-dee-uhEnzyme: A type of protein that helps break down substances in the body.Cell: The basic building block of the body and organs are made up of cells. They can have a variety of different functions.Real-world data: Observed health data changes in people with a condition over time. These can be routinely collected (with consent) at doctor’s appointments and entered into a registry or database.This is an abstract of the Plain Language Summary of Publication article.View the full Plain Language Summary PDF of this article to read the full-textLink to original article hereTrial registration: ClinicalTrials.gov identifier: NCT00196742.

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)0.001
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 score0.044
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.008
GPT teacher head0.298
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 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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