Successful Treatment of Refractory Post-Transplant Lymphoproliferative Disorder With Chimeric Antigen Receptor T-Cell Therapy in a Heart Transplant Recipient
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".