Posttransplant Lymphoproliferative Disorder in Children: A 360-degree Perspective
Bibliographic record
Abstract
Posttransplant lymphoproliferative disorder (PTLD) is the most common malignancy that can occur after organ transplant in children. PTLD arises because the patient’s immune system has been suppressed to protect the graft and may fail to provide an adequate immune check for transformed malignant or premalignant lymphocytes. PTLD risk factors and pathogenesis are not completely understood; however, Epstein-Barr virus is identified in many patients with PTLD and may contribute to its evolution. PTLD is a clinical challenge because the biologic behavior and clinical manifestations are diverse, and there is no standardized treatment. Treatment options include reduction of immunosuppression therapy, chemotherapy, radiation therapy, and surgical resection of localized lesions, depending on factors including the type and extent of disease, whether the patient has positive test results for the Epstein-Barr virus, and patient comorbidities. A multidisciplinary approach is essential for the appropriate management of PTLD. Diagnostic imaging modalities such as radiography, US, CT, MRI, and PET are essential in diagnosis, assessment of therapeutic response, and monitoring of this heterogeneous disease entity. When imaging findings are suggestive of PTLD, prompt tissue biopsy to identify the PTLD subtype is essential for appropriate treatment. It is important to know the proper indications and limitations of different diagnostic imaging techniques in the management of PTLD. In addition, familiarity with the imaging features of PTLD and its mimics narrows the differential diagnosis and may facilitate decision making about patient treatment. ©RSNA, 2020 An earlier incorrect version of this article appeared online. This article was corrected on December 17, 2019.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".