Antinuclear Antibody Multiplex Utilization Across a Large Federal Hospital System: An Investigation of Ordering Practices and Rheumatologic Outcomes
Bibliographic record
Abstract
OBJECTIVE: To understand the ordering patterns of antinuclear antibody (ANA) multiplex testing in a single, large US Department of Defense (DoD) tertiary healthcare system. METHODS: Records of patients with an ANA multiplex assay ordered over a 1-year period were evaluated in a large DoD hospital system. Duplicate tests and patients with a previously established autoimmune rheumatic disease (ARD) prior to the year of study were excluded. The remaining 2499 patients' charts were reviewed for clinical presentation, ordering specialty, ordering rationale, and whether subsequent rheumatology evaluations resulted in a new ARD diagnosis. RESULTS: The ANA multiplex assay was ordered most often by primary care and medicine subspecialties for > 100 reasons. In the ANA multiplex assay-negative group, 37/2228 (1.66%) individuals were diagnosed with a new ARD. In the ANA multiplex assay-positive group 37/271 (13.7%) individuals were diagnosed with a new ARD. Sjögren disease, systemic lupus erythematosus, and undifferentiated connective tissue disease were the most common newly diagnosed ARDs in the ANA multiplex assay-positive group. Rheumatoid arthritis and seronegative spondyloarthritis were the most common new ARD diagnoses in the ANA multiplex assay-negative group. In this study, 97% of the ordered ANA assays did not lead to an ARD diagnosis. CONCLUSION: This study demonstrates frequent utilization of the ANA multiplex assay in the evaluation of nonspecific signs and symptoms, with a low rate of ANA-associated ARDs suggesting a need for implementation of strategies to improve understanding of appropriate clinical contexts that warrant 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".