Scleroderma Lung Involvement, Autoantibodies, and Outcome Prediction: The Confounding Effect of Time
Bibliographic record
Abstract
Systemic sclerosis (SSc) remains a poorly understood disease and so far none of the routinely used immunosuppressive treatments has been definitively shown to benefit longterm disease outcome. Scleroderma-related cardiopulmonary involvement has been the leading cause of death in more recent decades, and pulmonary fibrosis (PF) and pulmonary hypertension (PH) account for a substantial proportion of the SSc-related deaths, as demonstrated by several large metaanalyses1,2,3. Early detection and monitoring of PF and PH may benefit outcome by permitting earlier intervention in severe or progressive cases4. Multiple attempts have been made to develop prediction models, both for development and for outcome, in already present SSc-related lung disease. Consistently, autoantibody specificities are among the strongest but not the only predictors of organ disease in scleroderma patients, and autoantibody characterization is a mandatory part of investigations in new SSc cases. It is well established that while positivity for anticentromere antibodies (ACA) is associated with the limited cutaneous subset of the disease and a low risk of pulmonary or renal involvement, presence of antitopoisomerase I antibodies (ATA) conveys a substantially increased risk of PF development; moreover anti-RNA polymerase antibodies (ARA) are strongly associated with the diffuse cutaneous subset (dcSSc) and development of scleroderma renal crisis (SRC)5. Most cohort studies describe no particular association between ARA positivity and PH or PF development. Although this was true in an unadjusted analysis, when correcting for other variables, ARA positivity was shown to associate with an increase in the hazard of PH development in a large single-center cohort analysis6. In addition, compared to ACA-positive patients, those carrying ARA have been … Address correspondence to Dr. S.I. Nihtyanova, Centre for Rheumatology and Connective Tissue Diseases, UCL Medical School, Royal Free Campus, Rowland Hill Street, London NW3 2PF, UK. E-mail: s.nihtyanova{at}ucl.ac.uk
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".