Prevalence and clinical profiles of 'autoantibody-negative' systemic sclerosis subjects.
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of autoantibody negative systemic sclerosis (SSc) and to identify the clinical correlates thereof. METHODS: Clinical data and sera from 874 SSc subjects were collected and autoantibodies were tested in a central laboratory using 1) indirect immunofluorescence (IIF), 2) commercially available ELISA, addressable laser bead immunoassay (ALBIA), and line immunoassay (LIA), and 3) a sensitive immunoprecipitation (IP) assay. RESULTS: Fifteen (15; 1.7%) subjects were autoantibody negative by IIF, ELISA, ALBIA, LIA and IP, and 16 (1.8%) were antinuclear antibody (ANA) positive by IIF but otherwise negative by ELISA, ALBIA, LIA and IP. Thirty-seven (37; 4.2%) were ANA positive by IIF, autoantibody negative by commercially available immunoassays, but had autoantibodies identified by IP (including Th/To in 20). Autoantibody-negative subjects had generally less severe disease than positive subjects. CONCLUSIONS: Autoantibody-negative SSc is rare (<2%) and appears to be associated with a favourable prognosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".