Bibliographic record
Abstract
INTRODUCTION: Systemic sclerosis (SSc) is a rare autoimmune connective tissue disease characterized by vascular dysfunction, fibrosis, inflammation and autoantibodies. The pathophysiology of SSc is not completely understood, and many patients acquire organ or tissue damage despite advances in treatment. Current treatments target affected organs with modest improvements. Areas covered: This review evaluates several treatment strategies for SSc based on involved organs including skin, pulmonary, cardiac, renal, musculoskeletal, and gastrointestinal. Currently, pulmonary hypertension and interstitial lung disease are the primary causes of increased mortality. We will outline an approach to treatment of SSc based on disease manifestations and current evidence. Expert commentary: This complex disease is currently treated with therapies developed for similar indications such as for vascular manifestations of SSc using idiopathic pulmonary arterial hypertension treatments. Future directions in this field may include combination and maintenance therapy that is currently used in other autoimmune diseases, and tailoring these treatments according to the patients' phenotype. This will hopefully increase the efficacy of available treatments and decrease mortality from SSc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 |
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".