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Record W3197087220 · doi:10.34172/jpe.2021.30

Infection-related immunoglobulin A nephropathy or IgA-dominant postinfectious glomerulonephritis; what is in a name?

2021· article· en· W3197087220 on OpenAlexaff
Ghazal Ghasempour Dabaghi, Mehrdad Rabiee Rad, Muhammed Mubarak, Romina Amir Sardari, Golnaz K Holm, Hamid Nasri

Bibliographic record

VenueJournal of Preventive Epidemiology · 2021
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGlomerulonephritisNephropathyDominance (genetics)ImmunologyBiologyRenal biopsyImmunoglobulin AAntibodyPathologicalPathologyMedicineBiopsyImmunoglobulin GKidneyGeneticsGene

Abstract

fetched live from OpenAlex

Immunoglobulin A nephropathy (IgAN) is the most common glomerulonephritis worldwide. However, its incidence and prevalence vary depending on racial and geographical factors. IgAN is a highly heterogeneous disease with wide clinical and pathological variability. The defining and consistent feature of IgAN is the dominance or co-dominance of IgA deposits in the glomeruli on immunofluorescence (IF) microscopy. However, recent reports suggest that a number of post-infectious glomerulonephritis (PIGN) cases also exhibit dominance or co-dominance of IgA deposits on IF microscopy. Therefore, a debate has arisen on labeling these cases either as infection-related IgAN (a form of secondary IgAN) or IgA-dominant PIGN. Although the majority favors the later nosology, this issue has remained unresolved, as is the issue of labelling this condition as PIGN when, in fact, the infection is often intercurrent, and no latent period is found in this condition. This brief narrative review aims to discuss the salient features of this condition and issues related to its nomenclature.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.023
GPT teacher head0.336
Teacher spread0.313 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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