Tailored Therapies for Patients Affected by Systemic Sclerosis with Primary Heart Involvement: The Role of Rituximab
Bibliographic record
Abstract
This article refers to ‘Add-on rituximab for primary heart involvement associated with systemic sclerosis: A step forward in the tailored treatment of myocarditis?’ by M. De Santis et al., published in this issue on pages 473–475. Systemic sclerosis (SSc) is a rare autoimmune connective tissue disease with multi-organ involvement.1 Classic manifestations include Raynaud phenomenon, skin involvement (limited or diffuse cutaneous SSc), gastrointestinal involvement, and SSc-interstitial lung disease (ILD), while less common manifestations can involve pulmonary arterial hypertension and cardiac manifestations (pericardial or cardiac involvement).1 Experts have proposed SSc with primary heart involvement (pHI) as a broad term to describe different cardiac manifestations.2 The prevalence of clinical SSc-pHI is still unknown and might be underestimated. In previous studies, the proportion of patients with SSc-pHI varies from 10% to 40% of patients with SSc, according to different applied definitions and different tools used to detect cardiac involvement.2,3 Common clinical cardiac features may include impaired contractility and relaxation, arrhythmias, myocarditis and pericardial disease. Despite uncertainty in definition and epidemiology, cardiac involvement is among the most frequent causes of death in SSc, representing a major issue for rheumatologists and cardiologists taking care of patients with this disorder.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".