MétaCan
Menu
Back to cohort
Record W4406606211 · doi:10.1016/j.ekir.2025.01.025

Using the International IgA Nephropathy Prediction Tool to Enrich Clinical Trial Cohorts

2025· article· en· W4406606211 on OpenAlexaff
Sean J. Barbour, Rosanna Coppo, Jonathan Barratt, Lee Er, Richard Lafayette, Hiddo J.L. Heerspink, Dana V. Rizk, Adrian Liew, Vivekanand Jha, Hong Zhang, Yusuke Suzuki, Hernán Trimarchi, Daniel Cattran

Bibliographic record

VenueKidney International Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of TorontoUniversity of British Columbia
FundersBioCrystNovartis Pharmaceuticals Corporation
KeywordsMedicineNephropathyClinical trialInternal medicineEndocrinologyDiabetes mellitus

Abstract

fetched live from OpenAlex

Clinical trials on IgA nephropathy (IgAN) have historically demonstrated a treatment effect on adverse outcomes, such as a 50% decline in the estimated glomerular filtration rate (eGFR) or kidney failure. Thus, trials required long follow-up, were expensive, with limited feasibility. This changed in 2016 when the Kidney Health Initiative analysis of 11 clinical trials demonstrated that a treatment effect on proteinuria over approximately 9 months was a reasonable surrogate for the effect on a hard kidney end point.

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)0.066
metaresearch head score (Gemma)0.207
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 score0.066
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.207
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0060.005
Science and technology studies0.0010.000
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.002

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 head0.044
GPT teacher head0.388
Teacher spread0.344 · 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 routes1
Has abstractyes

Explore more

Same venueKidney International ReportsSame topicRenal Diseases and GlomerulopathiesFrench-language works237,207