Measurement of antinuclear antibodies by multiplex immunoassay: a prospective, multicenter clinical evaluation.
Bibliographic record
Abstract
OBJECTIVE: We conducted a prospective, multicenter evaluation of autoantibody testing by multiplex immunoassay in patients with known or suspected connective tissue diseases (CTD). We evaluated agreement between multiplex immunoassay and enzyme immunoassay (EIA) and assessed the diagnostic utility of autoantibody profiles. METHODS: Samples from 908 patients with suspected CTD seen in rheumatology clinics were collected prospectively at 3 tertiary care centers. Diagnoses were established according to recognized classification criteria. Tests for autoantibodies were obtained by multiplex immunoassay and by EIA. The results of the multiplex immunoassay were analyzed using a previously validated interpretative algorithm, MDSS (Medical Decision Support Software), that suggests possible disease associations based on the pattern of results for the autoantibodies. RESULTS: The median patient age was 49.7 years; 83% were female. The most common diagnoses were rheumatoid arthritis in 352 patients and systemic lupus erythematosus (SLE) in 332 patients. Agreement between multiplex and EIA testing ranged from a high of 99% (95% CI 98% to 100%) for Jo-1 to a low of 79% (95% CI 76% to 82%) for antinuclear antibodies. The MDSS algorithm suggested an appropriate disease association in 75% to 100% of patients with SLE. The results varied depending on the disease and the autoantibodies present. CONCLUSION: These results suggest that patterns of autoantibodies detected by multiplex immunoassay testing, when analyzed by an interpretative algorithm, are useful in the evaluation of patients with CTD in situations of high disease prevalence. Further testing is necessary to determine its utility in settings of low disease prevalence.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".