Bibliographic record
Abstract
Targeted therapies use an understanding of the pathophysiology of a disease in an individual patient. Although targeted therapy for systemic sclerosis (SSc, scleroderma) has not yet reached the level of patient-specific treatments, recent developments in the understanding of the global pathophysiology of the disease have led to new treatments based on the cells and pathways that have been shown to be involved in the disease pathogenesis. The presence of a B cell signature in skin biopsies has led to the trial of rituximab, an anti-CD20 antibody, in SSc. The well-known properties of transforming growth factor (TGF)-β in promoting collagen synthesis and secretion has led to a small trial of fresolimumab, a human IgG4 monoclonal antibody capable of neutralizing TGF-β. Evidence supporting important roles for interleukin-6 in the pathogenesis of SSc have led to a large trial of tocilizumab in SSc. Soluble guanylate cyclase (sGC) is an enzyme that catalyzes the production of cyclic guanosine monophosphate (cGMP) upon binding of nitric oxide (NO) to the sGC molecule. Processes such as cell growth and proliferation are regulated by cGMP. Evidence that sGC may play a role in SSc has led to a trial of riociguat, a molecule that sensitizes sGC to endogenous NO. Tyrosine kinases (TKs) are involved in a wide variety of physiologic and pathological processes including vascular remodeling and fibrogenesis such as occurs in SSc. This has led to a trial of nintedanib, a next-generation tyrosine-kinase (TK) inhibitor which targets multiple TKs, in SSc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".