Improvement in Skin Fibrosis and Lung Function with Autologous Hematopoietic Stem Cell Transplantation in Systemic Sclerosis
Bibliographic record
Abstract
Systemic sclerosis (SSc) is a severe, progressive disease with limited treatment options. Autologous hematopoietic stem cell transplantation (AHSCT) has been shown to be an effective treatment for rapidly progressive SSc. The objective of this study was to evaluate the effectiveness of AHSCT for SSc compared to real-world clinical care. SSc patients from France who underwent AHSCT were compared to patients from Canada who met criteria for AHSCT (as defined in the ASTIS trial) but received conventional care. The primary outcome was overall survival. Secondary outcomes included modified Rodnan skin score (mRSS) and forced vital capacity (FVC). Overall survival was estimated by Kaplan-Meier survival curves. Measures of mRSS and FVC were compared using linear regression models. Analyses were adjusted for baseline scores and incorporated stabilized inverse probability of treatment weights to account for confounding by indication. Propensity scores were estimated using logistic regression. Forty-one AHSCT patients and 85 conventional care patients were compared. AHSCT was associated with a suggestive, though not statistically significant trend toward improvement in overall survival (log-rank P = .115). In follow-up, the mRSS was lower with AHSCT compared to conventional care: between group difference of 8.81; P ≤ .0001 at 12 months and 11.28; P = .011 at 60 months. There was no significant difference in FVC between groups at 12 months but at 24 months, AHSCT was associated with a higher FVC (between group difference of 10.53 (P = .05)). This study demonstrates with real-world long-term data that compared with conventional care, treatment with AHSCT may offer superior outcomes for SSc patients.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".