Survival outcomes of allogeneic hematopoietic cell transplants with EBV‐positive or EBV‐negative post‐transplant lymphoproliferative disorder, A CIBMTR study
Bibliographic record
Abstract
Abstract Background Post‐transplant lymphoproliferative disorders (PTLD) are associated with significant morbidity and mortality following allogeneic hematopoietic cell transplant (alloHCT). Although most PTLD is EBV‐positive (EBVpos), EBV‐negative (EBVneg) PTLD is reported, yet its incidence and clinical impact remain largely undefined. Furthermore, factors at the time of transplant impacting survival following PTLD are not well described. Methods Between 2002 and 2014, 432 cases of PTLD following alloHCT were reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). After exclusions, 267 cases (EBVpos = 222, 83%; EBVneg = 45, 17%) were analyzed. Results Two hundred and eight patients (78%) received in vivo T‐cell depletion (TCD) with either anti‐thymocyte globulin (ATG) or alemtuzumab. Incidence of PTLD was highest using umbilical cord donors (UCB, 1.60%) and lowest using matched related donors (MRD, 0.40%). Clinical features and histology did not significantly differ among EBVpos or EBVneg PTLD cases except that absolute lymphocyte count recovery was slower, and CMV reactivation was later in EBVneg PTLD [EBVpos 32 (5‐95) days versus EBVneg 47 (10‐70) days, P = .016]. There was no impact on survival by EBV status in multivariable analysis [EBVneg RR 1.42, 95% CI 0.94‐2.15, P = .097]. Conclusions There is no difference in survival outcomes for patients with EBVpos or EBVneg PTLD occurring following alloHCT and 1‐year survival is poor. Features of conditioning and use of serotherapy remain important.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".