Prevalence and Clinical Significance of Anti-DFS70 in Antinuclear Antibody (ANA)–positive Patients Undergoing Routine ANA Testing in a New Zealand Public Hospital
Bibliographic record
Abstract
To the Editor: The term antinuclear antibodies (ANA) originally referred to autoantibodies directed against nuclear antigens and antigens in the cell cytoplasm or membrane1. The presence of elevated ANA is considered as the hallmark diagnostic test for systemic autoimmune rheumatic diseases (SARD). In most New Zealand (NZ) laboratories, ANA are detected by indirect immunofluorescence test (IIF) on HEp-2. However, fluorescent patterns are sometimes difficult to interpret2,3. Recent advances in autoimmune technologies have emerged for ANA testing, and laboratories in NZ are moving into acquiring the required knowledge and skills. In our laboratory, ANA screening slides are interpreted by a NOVA View automated IIF slide reader (INOVA Diagnostics Inc.), which incorporates a digital analysis image system, pattern recognition algorithms, and preset cutoff values. Problems still exist for the laboratory community to determine whether this system efficiently identifies antigens of clinical significance and whether the different automated systems have an appropriate level of pattern recognition agreement4. The extractable nuclear antigen (ENA) panel is a test performed as … Address correspondence to Dr. F. Merien, Auckland University of Technology, School of Science, 34 St Paul St., Auckland 1142, New Zealand. E-mail: fmerien{at}aut.ac.nz
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".