Novel Clinical and Diagnostic Aspects of Antineutrophil Cytoplasmic Antibodies
Bibliographic record
Abstract
Antineutrophil cytoplasmic antibodies (ANCA) are the serological hallmark of some idiopathic systemic vasculitides. Besides the investigation of ANCA-associated vasculitis (AAV) and constant effort for a standardized nomenclature and classification of the AAV, a main focus of research during the last few years has been to constantly improve the performance of enzyme immunoassays. With the latest so called third generation ELISA, this goal seemed to be fulfilled. The International Consensus Statement on Testing and Reporting of ANCA gave recommendations for standardized strategies for the serological diagnosis of ANCA. New developments now target the system immanent drawbacks of the respective diagnostic methods, be it the need for batching and the long time to result for ELISA, or the high likelihood of error and subjectivity of indirect immunofluorescence (IIF). Random access technology and multiplexing for solid phase assays as well as digital imaging for IIF are tools which may help to expedite and simplify routine diagnostics in the lab and in emergency settings. Recent findings indicate that PR3-ANCA have clinical utility beyond the diagnosis of AAV. PR3-ANCA can also serve as an aid for the differentiation between ulcerative colitis (UC) and Crohn's disease (CrD) and the stratification of UC patients. This review provides a detailed review of what is known about ANCA and highlights the latest research and state-of-the-art developments in this area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".