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Record W4404020293 · doi:10.1111/ejh.14341

Daratumumab in the Management of Red Cell Aplasia Following Allogeneic Hematopoietic Stem Cell Transplantation

2024· article· en· W4404020293 on OpenAlexaff
Nihar Desai, Auro Viswabandya, Dennis Dong Hwan Kim, Jeffrey H. Lipton, Jonas Mattsson, Arjun Law

Bibliographic record

VenueEuropean Journal Of Haematology · 2024
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsDaratumumabMedicinePure red cell aplasiaHematopoietic stem cell transplantationAplastic anemiaOncologyTransplantationIntensive care medicineInternal medicineAnemiaMultiple myelomaBone marrowBortezomib

Abstract

fetched live from OpenAlex

Pure red cell aplasia (PRCA) is a rare but significant complication following major ABO-incompatible allogeneic hematopoietic stem cell transplantation (HSCT). The persistence of recipient B lymphocytes producing anti-donor isohemagglutinins leads to reticulocytopenia and anemia, often resulting in transfusion dependence. Current treatment options for post-HSCT PRCA are limited and frequently yield suboptimal responses, complicating patient management. Herein, we report three cases of post-HSCT PRCA successfully managed with daratumumab, a monoclonal antibody targeting CD38-expressing plasma cells. All patients demonstrated rapid reticulocyte recovery and transfusion independence after daratumumab treatment, despite prior treatment failures. These findings suggest that daratumumab may provide a more effective therapeutic approach, with a favorable safety profile compared to traditional therapies. Given its demonstrated efficacy and safety, daratumumab warrants consideration as a first-line treatment for post-HSCT PRCA, potentially improving patient quality of life and reducing transfusion-related complications. Further studies should explore optimal dosing and long-term outcomes.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.259
Teacher spread0.241 · 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 teacher head, not a consensus.

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

Citations8
Published2024
Admission routes1
Has abstractyes

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