International multi-center evaluation of a novel chemiluminescence assay for the detection of anti-dsDNA antibodies
Bibliographic record
Abstract
OBJECTIVE: Anti-double stranded desoxyribonucleic acid (anti-dsDNA) antibodies are considered fairly specific for systemic lupus erythematosus (SLE) and their quantification is useful for the clinical management of SLE patients. We assessed the diagnostic performance of the QUANTA Flash dsDNA chemiluminescent immunoassay (CIA) in comparison to an ELISA, using patients from five participating countries. The main focus was to evaluate the correlation between anti-dsDNA antibody results from the CIA and global SLE disease activity, as measured by the SLE Disease Activity Index 2000 (SLEDAI-2K). PATIENTS AND METHODS: A total of 1431 samples (SLE, n = 843; disease controls, n = 588) from five countries (Canada, USA, Portugal, Sweden and Spain) were tested with QUANTA Flash dsDNA (Inova Diagnostics, San Diego, CA, USA). Data obtained with the QUANTA Lite dsDNA SC ELISA (Inova Diagnostics) were available for samples from three sites (Canada, USA and Sweden, n = 566). The SLEDAI-2K scores were available for 805 SLE patients and a cut-off of > 4 was used to define active disease. RESULTS: QUANTA Flash dsDNA had a sensitivity of 54.3% for the diagnosis of SLE, combined with 89.8% specificity. Anti-dsDNA antibody levels were significantly higher (p < 0.0001) in active SLE (SLEDAI-2K > 4; n = 232; median value 83.0 IU/mL) versus the inactive patients (n = 573; median value 22.3 IU/mL), and the SLEDAI-2K scoring correlated with their dsDNA antibody levels (Spearman's rho = 0.44, p < 0.0001). Similar but less pronounced findings were also found for the ELISA, in relation to disease activity. CONCLUSIONS: The QUANTA Flash dsDNA assay showed good clinical performance in a large international multi-center study. Additionally, the strong correlation between anti-dsDNA antibody results and SLEDAI-2K scores supported the potential utility of QUANTA Flash dsDNA for monitoring disease activity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".