Detection of antinuclear antibodies: recommendations from EFLM, EASI and ICAP
Bibliographic record
Abstract
OBJECTIVES: Antinuclear antibodies (ANA) are important for the diagnosis of various autoimmune diseases. ANA are usually detected by indirect immunofluorescence assay (IFA) using HEp-2 cells (HEp-2 IFA). There are many variables influencing HEp-2 IFA results, such as subjective visual reading, serum screening dilution, substrate manufacturing, microscope components and conjugate. Newer developments on ANA testing that offer novel features adopted by some clinical laboratories include automated computer-assisted diagnosis (CAD) systems and solid phase assays (SPA). METHODS: A group of experts reviewed current literature and established recommendations on methodological aspects of ANA testing. This process was supported by a two round Delphi exercise. International expert groups that participated in this initiative included (i) the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group "Autoimmunity Testing"; (ii) the European Autoimmune Standardization Initiative (EASI); and (iii) the International Consensus on ANA Patterns (ICAP). RESULTS: In total, 35 recommendations/statements related to (i) ANA testing and reporting by HEp-2 IFA; (ii) HEp-2 IFA methodological aspects including substrate/conjugate selection and the application of CAD systems; (iii) quality assurance; (iv) HEp-2 IFA validation/verification approaches and (v) SPA were formulated. Globally, 95% of all submitted scores in the final Delphi round were above 6 (moderately agree, agree or strongly agree) and 85% above 7 (agree and strongly agree), indicating strong international support for the proposed recommendations. CONCLUSIONS: These recommendations are an important step to achieve high quality 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 | 0.092 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".