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Record W2324666099 · doi:10.1017/s0317167100004303

Nephrotic Syndrome in a Multiple Sclerosis Patient Treated with Interferon β1a

2005· article· en· W2324666099 on OpenAlexvenueno aff
Anthony Auty, Abdulkarim Saleh

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2005
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMultiple sclerosisNephrotic syndromeInterferon beta-1bToxicityInterferonInterferon betaGlomerulonephritisKidneyGastroenterologyInternal medicineImmunologyPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Interferon beta has become standard therapy for reducing relapse frequency in relapsing/remitting Multiple Sclerosis (RRMS). Several different preparations are available including interferon beta 1a (Avonex, Rebif) and interferon beta 1b (Betaferon/Betaseron). For the most part these preparations have been considered safe. Recently there have been concerns relating to liver and now kidney toxicity. CASE REPORT: We present a case of a 28-yr-old male who developed a severe case of nephrotic syndrome while being treated for relapsing/remitting Multiple Sclerosis (RRMS) with weekly injections of interferon beta 1a. SUBSEQUENT COURSE: The nephrosis resolved almost completely once the interferon was stopped and after immunosuppressive treatment. At its peak the daily protein loss was 35.82 g. Kidney biopsy demonstrated membranous glomerulonephritis. DISCUSSION: Two other case reports of nephrotic syndrome have been reported in the literature. This latest (third) report suggests that the safety profile should be reexamined and at least raises the question of potential renal toxicity of interferons in MS.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.277
Teacher spread0.198 · 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 designCase report
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

Citations21
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques→Same topicMultiple Sclerosis Research Studies→French-language works237,207→