Controversies: molecular vs. clinical systemic sclerosis classification
Bibliographic record
Abstract
Systemic sclerosis (SSc) is a multisystem chronic disease characterized by the three cardinal pathological features, including autoimmunity/inflammation, vasculopathy, and fibrosis, with unknown etiology. Individual patients manifest these three components to variable degrees, resulting in the diverse heterogeneity of clinical presentation. The classification of SSc patients into relatively homogenous subtypes is helpful in the setting of daily clinical practice and the field of clinical and basic research. The classification of SSc has been continuously discussed over four decades based on the clinical and laboratory features, especially the extent of skin sclerosis and disease-related autoantibodies. This clinical classification system enables clinicians to provide general advice regarding prognosis and risk for internal organ disease, but only permits estimates of outcomes informed by population-based studies. On the other hand, the recent decade has seen much progress in the understanding of molecular aspects of SSc complex pathology, raising a discussion on molecular classification of SSc. The development of molecular targeting therapies, especially biologics, further strengthens the importance of molecular classification which aids the identification of potential responders for each treatment. Although a careful validation study is required for molecular classification of SSc due to its large heterogeneity, the advance of molecular classification would introduce a further modification into SSc classification system in the near future. Importantly, clinical and molecular classifications are not mutually exclusive, therefore the combination would facilitate the development of a better classification system of this complex heterogeneous disorder that is useful in both the clinical setting and research studies.
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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 | 0.061 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".