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Record W4285807451 · doi:10.1016/j.blre.2022.100991

Blasts in context: the impact of the immune environment on acute myeloid leukemia prognosis and treatment

2022· review· en· W4285807451 on OpenAlexafffund
Yasmina Serroukh, Josée Hébert, Lambert Busque, François Mercier, Christopher E. Rudd, Sarit Assouline, Silvy Lachance, Jean‐Sébastien Delisle

Bibliographic record

VenueBlood Reviews · 2022
Typereview
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsMcGill UniversityJewish General HospitalLeukemia & Lymphoma Society of CanadaUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersFonds de Recherche du Québec - SantéWallonie-Bruxelles InternationalErasmus Medisch Centrum
KeywordsImmune systemBone marrowContext (archaeology)ImmunologyMyeloidTumor microenvironmentMyeloid leukemiaMedicineImmunotherapyStem cellLeukemiaCancer researchBiologyCell biology

Abstract

fetched live from OpenAlex

Acute myeloid leukemia (AML) is a cancer that originates from the bone marrow (BM). Under physiological conditions, the bone marrow supports the homeostasis of immune cells and hosts memory lymphoid cells. In this review, we summarize our present understanding of the role of the immune microenvironment on healthy bone marrow and on the development of AML, with a focus on T cells and other lymphoid cells. The types and function of different immune cells involved in the AML microenvironment as well as their putative role in the onset of disease and response to treatment are presented. We also describe how the immune context predicts the response to immunotherapy in AML and how these therapies modulate the immune status of the bone marrow. Finally, we focus on allogeneic stem cell transplantation and summarize the current understanding of the immune environment in the post-transplant bone marrow, the factors associated with immune escape and relevant strategies to prevent and treat relapse.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.054
GPT teacher head0.326
Teacher spread0.271 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations24
Published2022
Admission routes2
Has abstractyes

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