MétaCan
Menu
Back to cohort
Record W4321597742 · doi:10.3390/diagnostics13050851

Advanced Autoantibody Testing in Systemic Sclerosis

2023· review· en· W4321597742 on OpenAlexaff
Kholoud Almaabdi, Zareen Ahmad, Sindhu R. Johnson

Bibliographic record

VenueDiagnostics · 2023
Typereview
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsToronto Western HospitalUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsAutoantibodyMedicineAnti-nuclear antibodyImmunologyScleroderma (fungus)AntibodySystemic diseaseDiseasePathologyImmunopathology

Abstract

fetched live from OpenAlex

Systemic sclerosis is a systemic autoimmune rheumatic disease characterized by immune abnormalities, leading to vasculopathy and fibrosis. Autoantibody testing has become an increasingly important part of diagnosis and prognostication. Clinicians have been limited to antinuclear antibody (ANA), antitopoisomerase I (also known as anti-Scl-70) antibody, and anticentromere antibody testing. Many clinicians now have improved access to an expanded profile of autoantibody testing. In this narrative review article, we review the epidemiology, clinical associations, and prognostic value of advanced autoantibody testing in people with systemic sclerosis.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.218
GPT teacher head0.383
Teacher spread0.165 · 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
GenreReview

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

Citations12
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueDiagnosticsSame topicSystemic Sclerosis and Related DiseasesFrench-language works237,207