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Harmonized immune recovery monitoring after HCT: evidence and practical guidance from the Westhafen Intercontinental Group

2025· article· en· W4413965630 on OpenAlexaff
Taymour M. Hammoudi, Silvia Nucera, A. Lucas, Marc Ansari, Adriana Balduzzi, Alice Bertaina, Jochen Buechner, Selim Corbacioglu, Jean‐Hugues Dalle, Krzysztof Kałwak, Dean A. Lee, John E. Levine, Caroline A. Lindemans, Franco Locatelli, Roland Meisel, Stefan Nierkens, Giorgio Ottaviano, Antonio Pérez‐Martínez, Herbert Pichler, Susan E. Prockop, Michael A. Pulsipher, Julie‐An Talano, Sanjay Tewari, Kirk R. Schultz, Nirali N. Shah, Michael R. Verneris, Jaap Jan Boelens

Bibliographic record

VenueBlood Advances · 2025
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsBC Children's Hospital
FundersNIH Clinical CenterNational Cancer InstituteMedacSwedish Orphan Biovitrumbluebird bioAtara BiotherapeuticsMemorial Sloan-Kettering Cancer CenterLes Laboratories Pierre FabreCelgeneSanofiBristol-Myers SquibbNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsImmune systemTransplantationFlow cytometryMedicineHematopoietic cellCD8ImmunologyLimitingOncologyIntensive care medicineHaematopoiesisInternal medicineStem cellBiology

Abstract

fetched live from OpenAlex

ABSTRACT: Allogeneic hematopoietic cell transplantation (allo-HCT) is a curative option for patients with high-risk malignancies and nonmalignant disorders. Long-term survival depends on robust immune reconstitution (IR), which governs overall immune homeostasis and risks of infection, graft-versus-host disease, and relapse. However, despite its centrality to posttransplant outcomes, IR is not consistently monitored across transplant centers, limiting ability to generate meaningful, comparable, and translatable data. This review synthesizes current knowledge on numerical and functional IR milestones after allo-HCT, with a primary focus on flow cytometry-based monitoring of key immune cell subsets. Importantly, early CD4+ T-cell recovery (achieving >50 cells per μL by day 100 after transplant), is supported by strong clinical evidence and correlates with improved outcomes. Although emerging data suggest that additional subsets (CD8+ T cells, natural killer cells, B cells, naïve and recent thymic emigrant T cells, and γδ T cells) may also influence clinical trajectories, further harmonized, multicenter studies are needed to validate prognostic relevance across transplant settings. We propose practical, evidence-based guidelines for IR monitoring, including recommended time points, preferred assays, and flow cytometry panel components. Additionally, we highlight modifiable factors (eg, immunosuppressive drug exposures, graft manipulation) offering interventional opportunities for influencing IR. Harmonized monitoring strategies will support robust correlation between IR and clinical outcomes, guide real-time risk stratification, and facilitate the development of targeted, individualized transplant approaches. Standardization efforts led by consortia and registries are essential for advancing knowledge and optimizing care. We provide a roadmap for implementing uniform IR monitoring to improve outcomes and quality of life for allo-HCT recipients.

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)0.045
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.051
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0040.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.002

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 head0.020
GPT teacher head0.312
Teacher spread0.293 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2025
Admission routes1
Has abstractyes

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