ANTI-C1Q ANTIBODIES AS INDICATORS OF DISEASE ACTIVITY, RENAL INVOLVEMENT, AND NON-SCARRING ALOPECIA IN PATIENTS WITH SLE
Bibliographic record
Abstract
PV204 / #461 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose Anti-C1q antibodies are found in various autoimmune diseases and are believed to be associated with lupus nephritis (LN) in patients with systemic lupus erythematosus (SLE). Animal models have shown that anti-C1q antibodies lead to renal inflammation and damage. However, in addition to the association with LN, whether anti-C1q antibodies were linked to other SLE-related complications remains unknown. Methods This retrospective study analyzed 883 SLE patients at a medical center in Taiwan from 2017 to 2020 who met the American College of Rheumatology (ACR) 1990 or SLICC 2012 classification criteria. C1q CIC levels were measured within 1 month before or after diagnosis. Correlations of C1q CIC with other clinical manifestations were explored. Results We found that C1q CIC-positive patients were younger, with an average age of 38.8. Clinically, SLE patients presenting with non-scarring alopecia, renal disorder, and leukopenia had a significantly higher prevalence of C1q CIC positivity ( p < 0.05) (Table 1). Laboratory findings showed that C1q CIC-positive patients had elevated ESR, low complement levels, the presence of anti-dsDNA antibodies, and higher SLEDAI scores, suggesting that C1q antibodies are associated with active disease. Additionally, these patients had elevated UPCR and eGFR levels, indicating concurrent renal involvement. Table 1. Demographic data of participants Conclusions Our result suggests that C1q may be a biomarker for LN. The association of C1q with non-scarring alopecia warrants further investigation.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".