Failure of rituximab is associated with a poor outcome in diffuse large B cell lymphoma‐type post‐transplant lymphoproliferative disorder
Bibliographic record
Abstract
Post-transplant lymphoproliferative disorder (PTLD) may arise after solid organ transplantation, and the most common subtype resembles diffuse large B cell lymphoma (DLBCL). In DLBCL-type PTLD, the anti-CD20 antibody rituximab (R) may be combined with chemotherapy (R-CHOP) or use a strategy (R-primary; similar to the PTLD-1 clinical trial) consisting of induction with four weekly doses of R-alone, without any chemotherapy or sequential R-CHOP follow-up. Here we report on a multicentre retrospective cohort of solid organ transplant patients with DLBCL-type PTLD that were treated with R. In 168 adults, two-year overall survival (OS) was 63·7% [95% CI (confidence interval) 56·6-71·7%]. No difference in OS was observed, whether patients were treated with R-CHOP versus the R-primary strategy. In the 109 patients treated with R-primary, multivariate analysis found that baseline IPI score and the response to R-induction predicted OS. Patients who responded to R-induction had durable remissions without the addition of chemotherapy. Conversely, of the 46 patients who had stable or progressive disease after R-induction (R-failure), those who received R-CHOP had an only marginally improved outcome, with a two-year OS of 45% (23·1-65·3%) vs. no R-CHOP at 32% (14·7-49·8%). In real-world patients, R-failure and high IPI scores predict a poor outcome in DLBCL-type PTLD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".