Risk Factors for the Incidence of and the Mortality due to Post-Transplant Lymphoproliferative Disorder after Hematopoietic Cell Transplantation
Bibliographic record
Abstract
Post-transplant lymphoproliferative disorder (PTLD) is a potentially serious complication that occurs following hematopoietic cell transplantation (HCT), in which B cells transformed by Epstein-Barr virus (EBV) proliferate uncontrollably. It is unknown whether risk factors for the incidence of PTLD are identical to those for mortality due to PTLD, a clinically more important outcome. We sought to determine the risk factors influencing the incidence of PTLD and those influencing mortality due to PTLD in a cohort of 1184 allogenic HCT recipients. All patients were predisposed to PTLD, because their graft-versus-host disease (GVHD) prophylaxis included antithymocyte globulin. The overall PTLD incidence was 9.0%, and mortality due to PTLD was 1.1%. In multivariate analysis, risk factors for PTLD incidence include donor+/recipient- (D+/R-) EBV serostatus (subhazard ratio [SHR], 3.3; P = .002), use of a donor other than an HLA-matched sibling donor (non-MSD) (SHR, 1.7; P = .029), receipt of total body irradiation (TBI; SHR, 3.3; P = .008), and the absence of GVHD (SHR, 3.3; P < .001). The sole risk factor for mortality due to PTLD among all patients was D+/R- serostatus (SHR, 5.8; P = .022). Risk factors for mortality due to PTLD among patients who developed PTLD were use of a bone marrow (BM) graft (compared with peripheral blood stem cells [PBSCs]; SHR, 22.8; P < .001) and extralymphatic involvement (SHR, 14.6; P < .001). Interestingly, whereas the absence of GVHD was a risk factor for PTLD incidence, there was a trend toward the presence of GVHD as a risk factor for PTLD mortality (SHR, 4.2; P = .093). Likewise, whereas use of a BM graft was a risk factor for PTLD mortality, there was a trend toward use of a PBSC graft as a risk factor for PTLD incidence (SHR, 0.44; P = .179). Some risk factors for the incidence of PTLD are identical to the risk factors for mortality due to PTLD (ie, D+/R- serostatus), whereas other risk factors are disparate. Specifically, TBI was identified as a risk factor for PTLD incidence but not for PTLD mortality; the absence of GVHD was a risk factor for PTLD incidence, whereas the presence of GVHD was possibly a risk factor for PTLD mortality; and receipt of a PBSC graft was possibly a risk factor for PTLD incidence, whereas receipt of a BM graft was a risk factor for PTLD mortality.
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 | 0.002 |
| 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 | 0.000 | 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".