Evidence map›Paper›PMID 41691095›Full record

SynthesisBone marrow transplantation2026

CAR T-cell therapy in patients with acute lymphoblastic leukemia: a systematic review and meta-analysis.

Victor Navarro, Gloria Iacoboni, Sergi Camarillas, Cecilia Carpio, Mario Sánchez-Salinas, Samantha Feijoo, Francesc Bosch, Guillermo Villacampa, Pere Barba

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Bone marrow transplantation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Victor NavarroStatistics unit, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.ORCID 0000-0001-6925-4605
Gloria IacoboniDepartment of Hematology, University Hospital Vall d'Hebron, Barcelona, Spain.ORCID 0000-0003-0805-9288
Sergi CamarillasDepartment of Hematology, University Hospital Vall d'Hebron, Barcelona, Spain.
Cecilia CarpioDepartment of Hematology, University Hospital Vall d'Hebron, Barcelona, Spain.ORCID 0000-0001-9346-0134
Mario Sánchez-SalinasDepartment of Hematology, University Hospital Vall d'Hebron, Barcelona, Spain.
Samantha FeijooDepartment of Hematology, University Hospital Vall d'Hebron, Barcelona, Spain.
Francesc BoschDepartment of Hematology, University Hospital Vall d'Hebron, Barcelona, Spain.
Guillermo VillacampaStatistics unit, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.
Pere BarbaDepartment of Hematology, University Hospital Vall d'Hebron, Barcelona, Spain. pbarba@vhio.net.ORCID 0000-0001-7076-7969

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment landscape of relapsed/refractory (R/R) B-cell precursor acute lymphoblastic leukemia (B-ALL), with high remission rates across various CAR T-cell constructs. However, the durability of these responses remains a major challenge, with many patients experiencing relapse after an initial remission. This systematic review and meta-analysis aimed to compare the efficacy and safety of different CAR T-cell constructs across 40 clinical trials, including a total of 1540 R/R B-ALL patients. We assessed the impact of patient demographics, prior treatment exposure, and construct characteristics on treatment outcomes. The pooled complete remission rate (CRR) was 83.4% (I

Indexed as

Immunotherapy, AdoptivePrecursor Cell Lymphoblastic Leukemia-LymphomaReceptors, Chimeric AntigenHumansRemission InductionReceptors, Chimeric Antigen

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.