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Record W4408375863 · doi:10.1093/rheumatology/keaf011

Does plasma exchange have a role in ANCA-associated vasculitis? Viewpoint 1: plasma exchange should not be used indiscriminately

2025· review· en· W4408375863 on OpenAlexafffund
Michael Walsh

Bibliographic record

VenueLara D. Veeken · 2025
Typereview
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsHamilton Health SciencesImpactMcMaster UniversityPopulation Health Research Institute
FundersMedical Research Future FundNational Health and Medical Research CouncilMedical Research CouncilCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchBritish Heart FoundationAlexion PharmaceuticalsNierstichtingBayer
KeywordsMedicineAutoantibodyHarmKidney diseaseVasculitisRelative riskIntensive care medicinePlasmapheresisImmunologyDiseaseInternal medicinePsychologyAntibodyConfidence interval

Abstract

fetched live from OpenAlex

ANCA-associated vasculitis (AAV) is a heterogeneous autoimmune disease marked by varying organ involvement and outcomes. Plasma exchange, a method of removing native plasma and replacing it with crystalloid, albumin or donor plasma, can deplete autoantibodies and may help control autoimmune diseases rapidly. In AAV, several randomized controlled trials have been performed but, individually, had mixed results. The best data available, through meta-analysis, suggest the effects of plasma exchange in AAV are limited to improving kidney outcomes with a low likelihood of other benefits but also an increase in the risk of serious infections. As such, the use of plasma exchange in AAV should be limited to patients at risk of a poor kidney outcome as all others are more likely to experience harm than benefit. By estimating the risk of kidney failure and serious infections with and without plasma exchange, healthcare providers can help patients with AAV make informed choices about the use of plasma exchange.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.061
GPT teacher head0.322
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreCommentary

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 routes2
Has abstractyes

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