Historical Perspective on Antinuclear Antibody Testing
Bibliographic record
Abstract
Serum factors binding to cell nuclei were first described in the 1940s, and the antibodies responsible for the binding to self (autoantibodies) were discovered in the late 1950s. Routine standardized testing using a cell line (HEp-2) started in the 1980s and continues to evolve. In addition to the classic immunofluorescence assay (IFA), various immunochemical techniques have been developed for the measurement of antinuclear antibodies (ANAs). The complexity of ANA IFA pattern reading and the varying sensitivities, specificities, and overall clinical performance of the alternative methods have often generated controversies and sometimes even confusion among healthcare providers and laboratorians. A better understanding of the historical roots of ANA testing can aid in understanding these controversies and assist with selecting the best-performing methods. In this review, we present historic and contemporary ANA testing methods, highlighting the pros and cons of each. We also provide an overview of the current practice of ANA testing based on several recent large laboratory surveys. For optimal patient care, it is critical that clinicians and laboratorians using ANA testing understand the performance and limitations of the methods used by their institutions, as well as the meaning of the test results. Recently published surveys and standardization efforts initiated by several stakeholder scientific organizations will likely lead to new ANA diagnostic guidelines, to be followed by an improvement in testing practices, management, and outcomes for patients with autoimmune disorders.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".