Associations of the Levels of C4d-bearing Reticulocytes and High-avidity Anti-dsDNA Antibodies with Disease Activity in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: There are no laboratory tools that detect early flares in systemic lupus erythematosus (SLE). Our aim was to validate in our population the previous findings of the association of C4d-bearing reticulocytes (R-C4d) compared to anti-dsDNA antibodies, with disease activity assessed by the Safety of Estrogens in Lupus Erythematosus National Assessment-Systemic Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI) and the British Isles Lupus Assessment Group (BILAG) 2004 scales. METHODS: All patients who met the 1987 American College of Rheumatology classification criteria and were seen consecutively in 2013 at a specialized SLE care clinic were included. Disease activity was established by the SELENA-SLEDAI and BILAG 2004. Anti-dsDNA and R-C4d were quantified in peripheral blood. Comparisons were made between values of active and inactive patients, and the correlations between the SELENA-SLEDAI and serum levels of anti-dsDNA and R-C4d were measured. RESULTS: Sixty-two patients (83.9% women) were included. A total of 32.3% had active disease according to the SELENA-SLEDAI. There was a significant statistical difference (p = 0.0001) in the distribution of R-C4d between disease activity groups. The correlation coefficient between R-C4d and the SELENA-SLEDAI score was rs = 0.738 (p = 0.0001). R-C4d differed between patients with and without activity in the BILAG 2004 constitutional, mucocutaneous, gastrointestinal, renal, and hematological domains. CONCLUSION: R-C4d showed a higher correlation with SLE activity measured by the SELENA-SLEDAI and BILAG 2004 than anti-dsDNA did, suggesting a possible involvement in diagnosing disease activity. Prospective studies that confirm these findings and evaluate its involvement in followup are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".