Identifying the patient at risk for post‐transplant lymphoproliferative disorder
Bibliographic record
Abstract
Post-transplant lymphoproliferative disorders (PTLD) are a recognized complication of the immunosuppression required to prevent allograft rejection, occurring in 1-20% of recipients of solid organ transplants. Several factors greatly increase the risk of developing PTLD early post-transplant in any individual recipient. Epstein-Barr virus (EBV) infection is critical in the pathogenesis of the majority of these cases. Pre-transplant EBV seronegativity increases the incidence of PTLD 10- to 75-fold over that of EBV-seropositive recipients. Other risk factors include very young recipient age, cytomegalovirus infection or mismatching (donor positive-recipient negative), aggressive immunosuppression with conventional biologic agents, and the type of organ transplanted. In contrast, the risk of developing PTLD late in the post-transplant course does not appear to be influenced by the type of immunosuppressive agents employed, but rather by the duration of any immunosuppression. The role of EBV in late PTLD is also less certain, as a greater proportion of lesions are not associated with evidence of EBV infection. As the understanding of these risk factors has expanded, opportunities exist to target those populations at highest risk for the development of PTLD for aggressive monitoring and pre-emptive or prophylactic therapy. It is hoped that implementation of such strategies will render early PTLD a preventable complication of transplantation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".