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Record W2131897222 · doi:10.2215/cjn.06090809

Biomarkers of Fabry Disease Nephropathy

2009· article· en· W2131897222 on OpenAlexaff
Raphael Schiffmann, Stephen Waldek, Ariela Benigni, Christiane Auray‐Blais

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2009
Typearticle
Languageen
FieldMedicine
TopicLysosomal Storage Disorders Research
Canadian institutionsUniversité de Sherbrooke
FundersFondazione Internazionale MenariniAmicus Therapeutics
KeywordsMedicineEnzyme replacement therapyGlobotriaosylceramideFabry diseaseProteinuriaAlbuminuriaBiomarkerDiseaseInternal medicineClinical trialBiomarker discoveryNephropathyUrinary systemOncologyUrologyKidneyProteomicsEndocrinologyDiabetes mellitus

Abstract

fetched live from OpenAlex

It is suggested that biomarkers of renal complications of Fabry disease are likely to be useful for diagnosis and to follow the natural disease progression or the effect of specific therapeutic interventions. Traditionally, globotriaosylceramide (Gb(3)) in urine has been used to evaluate the effect of specific therapy, such as enzyme replacement therapy (ERT). Although urinary Gb(3) decreases significantly with ERT, it has not yet been shown to be a valid surrogate marker in treatment trials. We propose a detailed study of the nature and origin of Gb(3) combined with a prospective collaborative trial that combines Gb(3) changes with the effect of ERT on clinical nephrological outcome measures. Existing biomarkers such as general proteinuria/albuminuria or specific proteins such as N-acetyl-beta-D-glucosaminidase should be evaluated along with novel proteomic or metabolomic studies for biomarker discovery using mass spectrometry or nuclear magnetic resonance. Standard scoring of all pathologic aspects of kidney biopsies may also be a promising way to assess the effect of therapy.

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)0.003
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.389
Teacher spread0.345 · 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

Citations64
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueClinical Journal of the American Society of NephrologySame topicLysosomal Storage Disorders ResearchFrench-language works237,207