MétaCan
Menu
Back to cohort
Record W4394621491 · doi:10.14740/jh1211

Successful Treatment of Refractory Post-Transplant Lymphoproliferative Disorder With Chimeric Antigen Receptor T-Cell Therapy in a Heart Transplant Recipient

2024· article· en· W4394621491 on OpenAlexvenueno aff
Katherine Hickmann, Ryan Sweeney, Chelsea Peterson, Kathleen Faringer, Madeline Riley, Mark Bunker, Azam Hadi, Cyrus Khan, Yazan Samhouri

Bibliographic record

VenueJournal of Hematology · 2024
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChimeric antigen receptorRefractory (planetary science)Post-transplant lymphoproliferative disorderImmunologyCell therapyHeart transplantationImmunotherapyAntigenTransplant rejectionTransplantationCellLymphomaInternal medicineImmune systemBiologyRituximab

Abstract

fetched live from OpenAlex

Post-transplant lymphoproliferative disorders (PTLDs) are opportunistic malignancies that complicate the success of hematopoietic stem cell or solid organ transplantation. These disorders often arise post-transplant due to the immunosuppression required for minimizing the risk of rejection of donor tissue. First-line treatment of these disorders includes limiting immunosuppression when permissible. Subsequent treatment includes the use of monoclonal anti-CD20 antibody (rituximab), and/or combination chemotherapy. Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment paradigm in many lymphoid malignancies. It is not approved for PTLD due to exclusion of PTLD patients from pivotal clinical trials. Also, its utilization post-transplant can be complex and multidisciplinary care is of utmost importance for successful administration of a potentially curative treatment. We present a 68-year-old patient with history of heart transplant for non-ischemic cardiomyopathy, diagnosed with PTLD that was refractory to treatment using current guidelines until successfully receiving CAR T-cell therapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.300
Teacher spread0.284 · 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.

Study designBench or experimental
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

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of HematologySame topicCAR-T cell therapy researchFrench-language works237,207