MétaCan
Menu
Back to cohort
Record W2943920857 · doi:10.1016/j.ekir.2019.04.022

Persistent Mixed Cryoglobulinemia Despite Successful Treatment of Hepatitis C, Aggressive B-Cell–Directed Therapies, and Long-term Plasma Exchanges

2019· article· en· W2943920857 on OpenAlexaff
Karyne Pelletier, Virginie Royal, Frédéric Mongeau, Rosalie‐Sélène Meunier, Daniel Dion, Kevin Jao, Stéphan Troyanov

Bibliographic record

VenueKidney International Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsCegep de Saint JeromeHôpital Maisonneuve-RosemontUniversité de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsCryoglobulinsMedicineCryoglobulinemiaMacroglobulinemiaMonoclonalImmunologyWaldenstrom macroglobulinemiaImmunoglobulin MChronic lymphocytic leukemiaRheumatoid factorPolyclonal antibodiesMonoclonal gammopathy of undetermined significanceMultiple myelomaVirologyHepatitis C virusMonoclonal antibodyImmune systemRheumatoid arthritisAntigenAntibodyImmunoglobulin GLeukemiaLymphomaVirus

Abstract

fetched live from OpenAlex

Cryoglobulins are Igs that precipitate in cold temperature. Three subsets have been described: type I consists of only monoclonal Ig, usually due to monoclonal gammopathy of undetermined significance, multiple myeloma, Waldenström macroglobulinemia, or chronic lymphocytic leukemia.1 Type II cryoglobulins contain polyclonal IgG and a monoclonal IgM with rheumatoid factor (RF) activity directed against the IgG, whereas type III contains both polyclonal IgG and IgM RF.2 Types II and III, also called mixed cryoglobulinemia, result in most cases from a B-cell proliferative process in the setting of persistent immune activation triggered by chronic hepatitis C virus (HCV) infection.

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)0.001
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.014
GPT teacher head0.281
Teacher spread0.267 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueKidney International ReportsSame topicChronic Lymphocytic Leukemia ResearchFrench-language works237,207