Risk factors for post‐transplant lymphoproliferative disorder after Thymoglobulin‐conditioned hematopoietic cell transplantation
Bibliographic record
Abstract
Epstein-Barr virus (EBV)-induced post-transplant lymphoproliferative disorder (PTLD) occurs frequently when rabbit antithymocyte globulin (ATG) is used in hematopoietic cell transplant (HCT) conditioning. We retrospectively studied 554 patients undergoing ATG-conditioned myeloablative HCT. Strategies used to minimize mortality due to PTLD were either therapy of biopsy-diagnosed PTLD in the absence of EBV DNAemia monitoring (n = 266) or prompt therapy of presumed PTLD (based on clinical/radiologic signs and high EBV DNAemia, in the setting of weekly EBV DNAemia monitoring) (n = 199). Both strategies resulted in similar mortality due to PTLD (0.7% vs 1% at 2 years, P = .43) and similar overall survival (63% vs 67% at 2 years, P = .23) even though there was a trend toward higher PTLD incidence with the prompt therapy. Donor positive with recipient negative EBV (D+R-) serostatus was a risk factor for developing PTLD. Older patient age, HLA-mismatched donor, and graft-versus-host disease were not associated with increased risk of PTLD. In summary, in ATG-conditioned HCT, D+R- serostatus, but not older age, mismatched donor or GVHD is a risk factor for developing PTLD. EBV DNAemia monitoring may be a weak risk factor for developing/diagnosing PTLD; the monitoring coupled with prompt therapy does not improve survival.
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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 | 0.000 | 0.001 |
| 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".