Clinical and pathological features of pediatric peripheral T-cell lymphoma after solid organ transplantation
Bibliographic record
Abstract
Posttransplant lymphoproliferative disorder (PTLD) includes a broad spectrum of disorders, ranging from nonmalignant lymphoproliferation to lymphoma.[1][2][3][4][5] The clinical presentation of PTLD is variable, depending on the location and pathologic features.6 The majority of pediatric PTLD cases are Epstein-Barr virus (EBV)-positive, CD20 + B-cell lymphoma, whereas T-and natural killer (NK)-cell PTLDs (T/NK-PTLDs) are rare, representing <15% of PTLD cases, which usually occur at a median posttransplant interval of 4 to 6 years, and are associated with variable responses to treatment and overall poor outcome.[7][8][9][10][11] Peripheral T-cell lymphoma, not otherwise specified (PTCL, NOS) is the most common subtype of T/NK-PTLD, followed by anaplastic large cell lymphoma, hepatosplenic T-cell lymphoma, and cutaneous T-cell lymphoma.9,11 In contrast to B-cell PTLD, only 30% to 40% of T/NK-PTLD is EBV-related.5,11 EBV -T/NK-PTLD after solid organ transplantation (SOT) appear to have inferior survival than EBV + cases.7,9,11,12,14,15 T/NK-PTLD is extremely rare in children; hence, the pathological and clinical features of PTLD-PTCL in pediatric patients are largely unknown.Here, we report a multi-institutional case series of PTLD-PTCL and review the clinical, genetic, and pathological features of its presentation in a pediatric cohort.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".