Autoantibody Profile in Systemic Sclerosis
Bibliographic record
Abstract
Systemic sclerosis is a generalized disorder of connective tissue clinically characterized by thickening and fibrosis of the skin and by distinctive forms of involvement of internal organs. One of the hallmarks of systemic sclerosis is the presence of serum autoantibodies against a variety of nuclear and cytoplasmic antigens. The primary purpose of this study was to identify the autoantibodies profile in the scleroderma sera and the secondary goal was to determine the correlation and discrepancy of autoantibody profile. Autoantibody profile was determined in 118 samples stored in the Advanced Diagnostic Laboratory at the University of Calgary. 78 sera were provided from Canadian and 40 sera were provided from Ukraine. We used the following techniques to identify autoantibodies profile in scleroderma patients: 1. Antinuclear antibody (ANA) by indirect immunofluorescence on human epithelial cell substrate 2. Detection and identification of specific autoantibodies by Innolia strip assay 3. Detection and identification of specific autoantibodies against extractable nuclear antigens. 111 out of 118 patients showed positive ANA results by indirect immunofluorescence and 7 patients had negative ANA results. Anti-ENA analyses by Inolia were positive in 84 patients, while by western blotting 81 patients showed positive results. In this study, we compared the results of anti-ENA antibody by Innolia with SLR technique. A significant correlation was found between anti-SCl-70 antibodies (P=0.000) and anti- RNP antibodies (P=0.001) and JO-1 antibodies (P=0.014). Thus, we may propose that SLR and Innolia techniques could be used for the detection of autoantibody in systemic sclerosis.
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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 | 0.002 | 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) | 0.001 | 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".