Comparison of quantitative and qualitative anti-dsDNA assays
Bibliographic record
Abstract
OBJECTIVE: In evaluation of systemic lupus erythematosus (SLE), anti-double-stranded DNA antibodies (anti-dsDNA) play a significant role in diagnosis, monitoring SLE activity, and assessing prognosis. However, evaluations of the performance and limitations for recently developed methods for anti-dsDNA assessment are sparse. METHODS: Specimens used for antinuclear antibody testing (n = 129) were evaluated for anti-dsDNA assay comparability across 4 medical centers in the United States. The methods compared were Werfen Quanta Lite dsDNA, Zeus Scientific dsDNA Enzyme Immunoassay, Bio-Rad multiplex immunoassay (MIA) dsDNA, ImmunoConcepts Crithidia, and Bio-Rad Laboratories Crithidia. RESULTS: For quantitative anti-dsDNA measurements, Spearman's correlation coefficient was highest between Zeus and Werfen (ρ = 0.86; CI, 0.81-0.90; P < .0001). Comparison of MIA to Werfen or Zeus yielded similar results to each other (ρ = 0.58; CI, 0.44-0.68; P < .0001; and ρ = 0.59; CI, 0.46-0.69; P < .0001, respectively), but lower than the correlation between Zeus and Werfen. Positive concordance between assays ranged from 31.4% to 97.1%, and negative concordance between assays ranged from 58.5% to 100%. The detection of anti-dsDNA in those with SLE diagnosis ranged from 50.9% to 77.4% for quantitative assays and 15.1% to 24.5% for Crithidia assays. CONCLUSION: Current quantitative anti-dsDNA assays are not interchangeable for patient follow-up. Crithidia-based assays demonstrate high negative concordance and lack positive concordance among the methods.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".