Allogeneic stem-cell transplantation following chimeric antigen receptor T-cell therapy for treatment of relapsed/refractory hematologic malignancy in children and young adults: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Allogeneic stem cell transplantation (allo-SCT) and chimeric antigen receptor (CAR) T-cell therapy offer potential complementary benefits. PURPOSE: This study aimed to ascertain whether incorporating consolidative allo-SCT after CAR T-cell therapy can augment the therapeutic outcomes of child and young adult patients with relapsed/refractory hematologic malignancy. METHODS: A comprehensive literature search of PubMed, ScienceDirect, Cochrane Library, EBSCOHost, ProQuest, and the grey literature repositories was performed for articles published between May 5, 2014, and May 5, 2024. We included studies reporting consolidative allo-SCT following CAR T-cell therapy for treating hematologic malignancies in subjects aged ≤25 years old. The outcomes of interest were complete remission, survival, relapse, and mortality rates. The estimates were pooled using random-effects meta-analysis. The risk of bias was evaluated using the Newcastle-Ottawa Scale, while the certainty of evidence was assessed using GRADE. This study follows the PRISMA 2020 criteria and is registered in the PROSPERO database (CRD42023433417). RESULTS: Twelve cohort studies involving 380 patients, primarily those with B-cell acute lymphoblastic leukemia (B-ALL), were included. The CAR T-cell+SCT group showed a trend toward higher complete remission (odds ratio [OR], 2.74; 95% confidence interval [CI], 0.88-8.54; P= 0.08; I2=57%; evidence, very low); lower mortality (OR, 0.58; 95% CI, 0.27-1.27; P=0.17; I2=0%; evidence, low), and decreased relapse (OR, 0.18; 95% CI, 0.06-0.56; P=0.003; I2=41%; evidence, low) rates than those who did not proceed to SCT. In addition, both overall survival and leukemia-free survival rates showed a favorable trend toward the CAR T-cell+SCT group, respectively (hazard ratio, 0.44; 95% CI, 0.25-0.77; P=0.005; I2=0%; evidence, low; and hazard ratio, 0.29; 95% CI, 0.17-0.49; P<0.00001; I2=0%; evidence, low). Common posttransplant toxicities include mild to moderate acute and chronic graft-versus-host diseases. CONCLUSION: Although the current level of evidence remains low or very low, allo-SCT following CAR T-cell infusion potentially benefits patient survival. Further clinical studies are required to confirm these findings.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".