The Clinical Significance of the Dense Fine Speckled Immunofluorescence Pattern on HEp-2 Cells for the Diagnosis of Systemic Autoimmune Diseases
Bibliographic record
Abstract
Antinuclear antibodies (ANAs) are a serological hallmark in the diagnosis of systemic autoimmune rheumatic diseases (SARD). The indirect immunofluorescence (IIF) assay on HEp-2 cells is a commonly used test for the detection of ANA and has been recently recommended as the screening test of choice by a task force of the American College of Rheumatology. However, up to 20% of apparently healthy individuals (HI) have been reported to have a positive IIF ANA test, primarily related to autoantibodies that target the dense fine speckles 70 (DFS70) antigen. Even more important, the DFS IIF pattern has been reported in up to 33% of ANA positive HI, but not in ANA positive SARD sera. Since the intended use of the ANA HEp-2 test is to aid in the diagnosis and classification of SARD, the detection and reporting of anti-DFS70 antibodies and their associated pattern (DFS) as a positive test significantly reduce the specificity and the positive likelihood of the ANA test. This has significant implications for medical management and diagnostic algorithms involving the detection of ANA. Recently, a novel immunoadsorption method has been developed that specifically blocks anti-DFS70 antibodies and, therefore, significantly increases the specificity of the ANA test for SARD. This immunoadsorption method has the potential to overcome a significant limitation of the ANA HEp-2 assay. The present paper summarizes the current knowledge about anti-DFS70 antibodies and their clinical impact on ANA testing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".