The Impact of Graft-versus-Host Disease on the Relapse Rate in Patients with Lymphoma Depends on the Histological Subtype and the Intensity of the Conditioning Regimen
Bibliographic record
Abstract
The purpose of this study was to analyze the impact of graft-versus-host disease (GVHD) on the relapse rate of different lymphoma subtypes after allogeneic hematopoietic cell transplantation (allo-HCT). Adult patients with a diagnosis of Hodgkin lymphoma, diffuse large B cell lymphoma, follicular lymphoma (FL), peripheral T cell lymphoma, or mantle cell lymphoma (MCL) undergoing HLA-identical sibling or unrelated donor hematopoietic cell transplantation between 1997 and 2009 were included. Two thousand six hundred eleven cases were included. A reduced-intensity conditioning (RIC) regimen was used in 62.8% of the transplantations. In a multivariate analysis of myeloablative cases (n = 970), neither acute (aGVHD) nor chronic GVHD (cGVHD) were significantly associated with a lower incidence of relapse/progression in any lymphoma subtype. In contrast, the analysis of RIC cases (n = 1641) showed that cGVHD was associated with a lower incidence of relapse/progression in FL (risk ratio [RR], .51; P = .049) and in MCL (RR, .41; P = .019). Patients with FL or MCL developing both aGVHD and cGVHD had the lowest risk of relapse (RR, .14; P = .007; and RR, .15; P = .0019, respectively). Of interest, the effect of GVHD on decreasing relapse was similar in patients with sensitive disease and chemoresistant disease. Unfortunately, both aGVHD and cGVHD had a deleterious effect on treatment-related mortality and overall survival (OS) in FL cases but did not affect treatment-related mortality, OS or PFS in MCL. This study reinforces the use of RIC allo-HCT as a platform for immunotherapy in FL and MCL patients.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".