The Past, Present and Future of Antinuclear Antibody (ANA) Testing
Bibliographic record
Abstract
This article is in memory of Dr. Eng M. Tan (Emeritus: The Scripps Research Institute) and acknowledges the remarkable mentorship and tremendous contributions to our understanding of anti-nuclear antibodies (ANA). Dr. Tan passed away in 2024 at the age of 97. More than 70 years have passed since the discovery of the lupus erythematosus (LE) cell and the development of the LE cell test, which led to the ‘tipping point’ for the discovery of antinuclear antibody (ANA), or what should more correctly be referred to as anti-cellular antibodies (ACA). Paralleling the evolution of ANA testing based on the indirect immunofluorescence assay (IFA) on cryopreserved organ sections in the 1960s and through the early 1970s was an ‘explosion’ in the spectrum of ANA and a remarkable transition in technologies used to detect ANA. This included the transition to IFA on HEp-2 cell substrates beginning in the late 1970s. While some of the ‘octogenarian’ immunoassays such as double immunodiffusion, hemagglutination, complement fixation, radioimmunoassay, and counterimmunoelectrophoresis are fading into oblivion, the ANA IFA has prevailed because of its world-wide use as a screening test for systemic autoimmune rheumatic diseases (SARD), diagnostic criteria for autoimmune hepatitis, a risk factor for the development of uveitis in juvenile idiopathic arthritis, and the entry criterion for classification of systemic lupus erythematosus (SLE). ANA testing, once regarded the domain of rheumatologists and clinical immunologists, has witnessed a widening spectrum of clinicians using these tests because of its links to a growing spectrum of autoimmune and autoinflammatory conditions. All of this is set against the background of remarkable advances in autoantibody detection, especially the emergence of newer high-throughput (i.e., faster turn-around‑time for results), multi-analyte array technologies (MAAT). These technologies use comparatively small serum or plasma volumes and provide higher specificity while detecting a broad range of SARD autoantibodies.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".