Antinuclear Antibodies Testing Method Variability: A Survey of Participants in the College of American Pathologists’ Proficiency Testing Program
Bibliographic record
Abstract
OBJECTIVE: This study was conducted to determine the spectrum of laboratory practices in antinuclear antibody (ANA) test target, performance, and result reporting. METHODS: A questionnaire on ANA testing was distributed by the Diagnostic Immunology and Flow Cytometry Committee of the College of American Pathologists (CAP) to laboratories participating in the 2016 CAP ANA proficiency survey. RESULTS: Of 5847 survey kits distributed, 1206 (21%) responded. ANA screening method varied: 55% indirect immunofluorescence assay, 21% ELISA, 12% multibead immunoassay, and 18% other methods. The name of the test indicated the method used in only 32% of laboratories; only 39% stated the method used on the report. Of 644 laboratories, 80% used HEp-2 cell substrate, 18% HEp-2000 (HEp-2 cell line engineered to overexpress SSA antigen, Ro60), and 2% other. Slides were prepared manually (67%) or on an automated platform (33%) and examined by direct microscopy (84%) or images captured by an automated platform (16%). Only 50% reported a positive result at the customary 1:40 dilution. Titer was reported to endpoint routinely by 43%, only upon request by 23%, or never by 35%. Of the laboratories, 8% did not report dual patterns. Of those reporting multiple patterns, 23% did not report a titer with each pattern. CONCLUSION: ANA methodology and practice, and test naming and reporting varies significantly between laboratories. Lack of uniformity in testing and reporting practice and lack of transparency in communicating the testing method may misdirect clinicians in their management of patients.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".