Associations with Organ Involvement and Autoantibodies in Systemic Sclerosis: Results from the Canadian
Bibliographic record
Abstract
Copyright © 2013 Vikram Tangri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective: Serum from SSc patients was analyzed centrally to determine ANA patterns and extractable nuclear anti-gens (ENAs) between lcSSc and dcSSc and associations with organ involvement. Methods: 1145 SSc patients had ANA and ENA analyzed by indirect immunofluorescence on HEp-2 substrate at a screening serum dilution of 1/160. Most ENA antibodies [Sm. U1-RNP, Ro52, SS-A/Ro60, topoisomeraseI (Topo1), SS-B/La, chromatin, ribosomal P and Jo1] were measured by laser bead immunoassay; and RNA polymerase III (RNAP) by ELISA. Results: ANA was posi-tive in 95 % (same in lcSSc, and dcSSc). Centromere pattern was present in 34%, speckled 22%, nucleolar 18%, homo-geneous and speckled (H&S) 16%, multiple nuclear dots 6%. Anti-centromere Ab (ACA) occurred in 46 % of lcSSc and 11 % of dcSSc (P = 0.0001). ENAs that differed between lcSSc and dcSSc subsets were Topo1 (OR 2.4, P = 0.0001) and RNAP (OR 5.6, P < 0.0001) more common in dcSSc. Overall, 15 % had positive Topo1; usually with a H&S pattern (67%); Topo1 was associated with ILD on CXR (OR 2.3; 95 % CI 1.5- 3.5) and HRCT (OR 3.8; 95 % CI 1.8- 8.2). RNAP occurred in 18.5 % (35.4 % in dcSSc vs. 8.9 % in lcSSc). Scleroderma renal crisis (SRC) was 13 times more likely if RNAP positive; P = 0.0001. ACA was only weakly associated with sPAP> 50 mmHg (OR 1.8; 95%CI 1.1- 3.0).
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".