Cell bound complement activation products alone and in combination with low serum complement C3 or C4 have superior diagnostic performance in systemic lupus erythematosus
Bibliographic record
Abstract
Abstract BACKGROUND Cell-bound complement activation products (CB-CAPs) are sensitive and specific diagnostic markers of systemic lupus erythematosus (SLE). We compared the performance of CB-CAPs to low serum complement C3 or C4 in distinguishing SLE from other rheumatic diseases and healthy individuals. METHODS Adult subjects (n=1200) were enrolled from multiple academic centers, including SLE (498), healthy individuals (252) and subjects with other rheumatic diseases (450). Erythrocyte bound C4d [EC4d] and B-Lymphocyte bound C4d [BC4d] were quantitated using flow cytometry. Serum C3 and C4 levels were determined using immunoturbidimetry. Measurements included sensitivity, specificity, area under the curve (AUC) of the receiver operating characteristic curve (ROC) and Youden Index, for each marker as well as combinations. RESULTS Abnormal CB-CAPs status yielded 62% sensitivity with 88% specificity in distinguishing SLE from the group with other diseases compared to low C3/C4 status − 38% sensitivity, 93% specificity. Youden index was 0.492±0.03 for CB-CAPs compared to 0.313±0.03 for low C3/C4 (p<0.01). AUC was higher with BC4d (0.72) than with EC4d (0.68; p<0.01), low C3 (0.62; p<0.01), low C4 (0.62; p<0.01) and low C3 and/or C4 levels (0.66; p<0.01). The cumulative complement scoring system yielded higher AUC (0.81). A score with greater than 1 complement abnormality yielded 45% sensitivity and 98% specificity. CONCLUSION Our data suggests that CB-CAPs have greater diagnostic performance than low serum complement C3/C4. The combination of these complement abnormalities in a composite complement score is superior in distinguishing SLE from other rheumatic diseases and healthy individuals.
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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 | 0.004 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".