Antinuclear antibodies (ANA) as a criterion for classification and diagnosis of systemic autoimmune diseases
Bibliographic record
Abstract
The classification and diagnosis of systemic autoimmune diseases are frequently based on a collection of criteria composed of clinical, laboratory, imaging, and pathology elements that are strongly associated with the respective disease. Autoantibodies are a distinctive hallmark and have a prominent position in the classification criteria of many autoimmune diseases. The indirect immunofluorescence assay on HEp-2 cells (HEp-2 IFA), historically known as the antinuclear antibody test, is a method capable of detecting a wide spectrum of autoantibodies. A positive HEp-2 IFA test is part of the classification criteria for systemic lupus erythematosus (SLE) and juvenile idiopathic arthritis (JIA), as well as the diagnostic criteria for autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC). A positive HEp-2 IFA test can appear as different morphological patterns that are indicative of the most probable autoantibody specificities in the sample. Only some of the HEp-2 IFA patterns are associated with the specific autoantibodies relevant to SLE, JIA, AIH, and PBC, whereas some other patterns occur mainly in non-related conditions and even in apparently healthy individuals. This paper provides a critical review on the subject and proposes that the classification and diagnostic criteria for SLE, JIA, AIH, and PBC could be improved by a modification on the HEp-2 IFA (ANA) criterion in that the staining patterns accepted for each of these diseases should be restricted according to the respective relevant autoantibody specificities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".