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Record W4220986427 · doi:10.3389/fped.2022.796994

Is It Possible to Separate the Graft-Versus-Leukemia (GVL) Effect Against B Cell Acute Lymphoblastic Leukemia From Graft-Versus-Host Disease (GVHD) After Hematopoietic Cell Transplant?

2022· review· en· W4220986427 on OpenAlexaff
Jacob Rozmus, Sima Bhatt, Nataliya P. Buxbaum, Geoff D.E. Cuvelier, Amanda M. Li, Carrie L. Kitko, Kirk R. Schultz

Bibliographic record

VenueFrontiers in Pediatrics · 2022
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of ManitobaCancerCare ManitobaBC Children's HospitalUniversity of British Columbia
FundersCancer Research Institute
KeywordsMedicineLeukemiaGraft-versus-host diseaseHematopoietic stem cell transplantationLymphoblastic LeukemiaDiseaseImmunologyEx vivoTransplantationHaematopoiesisMinimal residual diseaseStem cellHematopoietic stem cellIn vivoInternal medicineBiology

Abstract

fetched live from OpenAlex

Hematopoietic cell transplant is a curative therapy for many pediatric patients with high risk acute lymphoblastic leukemia. Its therapeutic mechanism is primarily based on the generation of an alloreactive graft-versus-leukemia effect that can eliminate residual leukemia cells thus preventing relapse. However its efficacy is diminished by the concurrent emergence of harmful graft-versus-host disease disease which affects healthly tissue leading to significant morbidity and mortality. The purpose of this review is to describe the interventions that have been trialed in order to augment the beneficial graft-versus leukemia effect post-hematopoietic cell transplant while limiting the harmful consequences of graft-versus-host disease. This includes many emerging and promising strategies such as ex vivo and in vivo graft manipulation, targeted cell therapies, T-cell engagers and multiple pharmacologic interventions that stimulate specific donor effector cells.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.023
GPT teacher head0.304
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

Explore more

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