MétaCan
Menu
Back to cohort

109 Cross-sectional study of seven cases of IgG4 related disease

2018· article· en· W2799524128 on OpenAlexaff
Remesh Bhasi

Bibliographic record

VenueLara D. Veeken · 2018
Typearticle
Languageen
FieldMedicine
TopicIgG4-Related and Inflammatory Diseases
Canadian institutionsASTER
Fundersnot available
KeywordsMedicineCross-sectional studyIgG4-related diseaseDiseaseInternal medicinePathology

Abstract

fetched live from OpenAlex

Background: IgG4 RD is a cluster of disorders, which has been increasingly recognised over the past few years with certain common characteristics. Apart from the multi organ involvement IgG4 RD also has lymphocytic infiltrate rich in plasma cells with storiform pattern as a common attribute. In addition response to glucocorticoid therapy is also a recognised feature for the diagnosis of IgG4 RD. Methods: 7 cases admitted for evaluation of symptoms pertaining to various systems, presenting with varied presentations were investigated and found to have IgG4 RD Results: The study subjects in the case series belonged to multiple age groups from age of 27 years to 72 years. M: F ratio was 4:3. Generalised lymphadenopathy was the most common presentation which prompted the further investigation in lines of IgG4 RD with three patients having the same. While one patient presented with autoimmune pancreatitis, two subjects had retroperitoneal fibrosis as the presenting feature. The diagnosis of IgG4RD was a coincidental finding in one patient who was evaluated for fatigue and found to have kidney disease. All the patients had elevated IgG4 levels. Two patients had rheumatoid factor positive from before and was being treated accordingly. Antinuclear antibody and anti neutrophil cytoplasmic antibody was positive in one of the patients. Three of the patients had biopsy consistent with IgG4RD. Conclusion: IgG4 RD still remains an elusive disease and often under diagnosed. There was an average time gap of two years for the patient to be diagnosed with IgG4RD. Serum IgG4 levels proved vital for the identification. All the patients responded well to steroids and are under constant follow up Disclosures: The author has declared no conflicts of interest.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.303
Teacher spread0.283 · 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 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

Citations0
Published2018
Admission routes1
Has abstractno

Explore more

Same venueLara D. VeekenSame topicIgG4-Related and Inflammatory DiseasesFrench-language works237,207