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Record W2110192851 · doi:10.1681/asn.2011080775

Rituximab in Children with Resistant Idiopathic Nephrotic Syndrome

2012· article· en· W2110192851 on OpenAlexaff
Alberto Magnasco, Pietro Ravani, Alberto Edefonti, Luisa Murer, Luciana Ghio, Mirco Belingheri, Elisa Benetti, Corrado Murtas, Giovanni Messina, Laura Massella, Maria Gabriella Porcellini, Michela Montagna, Mario Regazzi, Francesco Scolari, Gian Marco Ghiggeri

Bibliographic record

VenueJournal of the American Society of Nephrology · 2012
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of Calgary
FundersMinistero della Salute
KeywordsRituximabMedicineNephrotic syndromeCalcineurinPrednisoneProteinuriaInternal medicineNephrologyGastroenterologyTransplantationKidneyLymphoma

Abstract

fetched live from OpenAlex

Idiopathic nephrotic syndrome resistant to standard treatments remains a therapeutic dilemma in pediatric nephrology. To test whether the anti-CD20 monoclonal antibody rituximab may benefit these patients, we conducted an open-label, randomized, controlled trial in 31 children with idiopathic nephrotic syndrome unresponsive to the combination of calcineurin inhibitors and prednisone. All children continued prednisone and calcineurin inhibitors at the doses prescribed before enrollment, and one treatment group received two doses of rituximab (375 mg/m(2) intravenously) as add-on therapy. The mean age was 8 years (range, 2-16 years). Rituximab did not reduce proteinuria at 3 months (change, -12% [95% confidence interval, -73% to 110%]; P=0.77 in analysis of covariance model adjusted for baseline proteinuria). Additional adjustment for previous remission and interaction terms (treatment by baseline proteinuria and treatment by previous remission) did not change the results. In conclusion, these data do not support the addition of rituximab to prednisone and calcineurin inhibitors in children with resistant idiopathic nephrotic syndrome.

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)0.000
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 score0.027
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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".

Quick stats

Citations174
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society of NephrologySame topicRenal Diseases and GlomerulopathiesFrench-language works237,207