Mesenchymal stromal cells for the prophylaxis of graft-versus-host disease after hematopoietic stem cell transplantation: A meta-analysis of randomized controlled trials.
Bibliographic record
Abstract
e18549 Background: Mesenchymal stromal cells (MSCs) have emerged as a potential alternative therapeutic strategy for the prophylaxis of graft-versus-host disease (GVHD) in patients undergoing hematopoietic stem cell transplantation (HSCT). Methods: This meta-analysis included eight randomized controlled trials (RCTs) involving 570 patients. The primary outcomes assessed were overall survival (OS), the development of acute GVHD (aGVHD), and chronic GVHD (cGVHD). Secondary outcomes included primary disease relapse and adverse events. The statistical analysis was performed using Review Manager (RevMan 5.4) with a random-effects model. Results: The meta-analysis showed a significant improvement in overall survival in the MSC group compared to the control group (RR 1.12; 95% CI: 1.02-1.23), with no evidence of heterogeneity (I²=0%). MSC prophylaxis was associated with a significant reduction in the incidence of aGVHD (RR 0.67; 95% CI: 0.40-0.83, I²=33%) and cGVHD (RR 0.65; 95% CI: 0.49-0.87, I²=0%). However, no significant difference was found between the MSC and control groups regarding primary disease relapse (RR 1.00; 95% CI: 0.73-1.38, I²=0%) or the incidence of infections (RR 0.80; 95% CI: 0.57-1.11, I²=0%). In terms of patients with at least one adverse event, no statistically significant difference was observed between the two groups (RR 1.10; 95% CI: 0.74-1.63, I²=34%). Conclusions: MSC prophylaxis significantly improves overall survival and reduces the incidence of both aGVHD and cGVHD in HSCT patients, without increasing the risk of relapse, infections, or adverse events, indicating its potential as a safe and effective intervention for GVHD management.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".