Bibliographic record
Abstract
INTRODUCTION: Scleroderma is an often-fatal autoimmune connective tissue disease. Recommendations for treating digital ulcers and pulmonary hypertension in scleroderma have recently been established by the European League Against Rheumatism. Conversely, although many valuable insights have been generated into the molecular mechanism underlying the persistent fibrotic phenotype in scleroderma, no safe, clinically proven effective treatment has been found for this aspect of the disease. AREAS COVERED: Recent evidence suggests that, based on genome-wide molecular profiling, scleroderma can be loosely divided into 'fibroproliferative' and 'inflammatory' cohorts. The latter cohort contains patients with localized and 'limited' disease, as well as a small subset of those with 'diffuse' disease. Drugs targeting either B cells or ILs might be useful to treat patients who possess an 'inflammatory' gene expression signature. EXPERT OPINION: In the future, a 'personalized medicine' approach might be used to treat patients with scleroderma: individuals with an 'inflammatory' gene expression signature may be successfully treated with drugs specifically targeting the immune system. Indeed, drugs currently approved for other rheumatic disease might also be used to treat scleroderma patients bearing an 'inflammatory' gene expression profile.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".