Hypocomplementemia in systemic sclerosis--clinical and serological correlations.
Bibliographic record
Abstract
OBJECTIVE: Although complement fixation is not commonly thought to be part of the pathogenesis of systemic sclerosis (SSc), hypocomplementemia has been associated with SSc. We hypothesized that hypocomplementemia in SSc might indicate the presence of overlap disease. We investigated if SSc patients with hypocomplementemia had more features of overlap disease than those with normal complement levels. METHODS: Study subjects consisted of those enrolled in the Canadian Scleroderma Research Group Registry. Patients were divided into 2 groups: those with normal complement levels (normal C3 and C4) and those with hypocomplementemia (low C3 or C4). Evidence of overlap disease was defined as physician reports of other specific rheumatic conditions. Autoantibodies were assayed. Differences in rates of concomitant diseases and in antibody profiles were compared between groups. RESULTS: Our study included 321 patients (88% women, mean age 56 +/- 13 yrs, mean disease duration 11 +/- 9 yrs). Of these, 276 (86%) had normal complements and 45 (14%) had hypocomplementemia. Patients with hypocomplementemia were significantly more likely to have physician-reported inflammatory myositis (27% vs 12%; p < 0.008) and vasculitis (11% vs 2%; p < 0.011) than those with normal complement. There was also a trend toward more antichromatin antibodies (18% vs 9%; p = 0.051) in patients with hypocomplementemia compared to normals. CONCLUSION: Hypocomplementemia may identify a particular subgroup of SSc patients who have overlap disease.
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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 | 0.002 |
| 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) | 0.003 | 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".