Lymphoma and cerebral vasculitis in association with X-linked lymphoproliferative disease
Bibliographic record
Abstract
Lymphoma is seen in up to 30% of patients with X-linked lymphoproliferative disease (XLP), but cerebral vasculitis related with XLP after cure of Burkitt lymphoma is rarely reported. We describe a case of a 5-year-old boy with XLP who developed cerebral vasculitis two years after cure of Burkitt lymphoma. He had Burkitt lymphoma at the age of 3 years and received chemotherapy (non-Hodgkin's lymphoma-Berlin-Frankfurt-Milan-90 protocol plus rituximab), which induced complete remission over the following two years. At the age of 5 years, the patient first developed headache, vomiting, and then intellectual and motorial retrogression. His condition was not improved after anti-infection, dehydration, or dexamethasone therapy. No tumor cells were found in his cerebrospinal fluid. Magnetic resonance imaging showed multiple non-homogeneous, hypodense masses along the bilateral cortex. Pathology after biopsy revealed hyperplasia of neurogliocytes and vessels, accompanied by lymphocyte infiltration but no tumor cell infiltration. Despite aggressive treatment, his cognition and motor functions deteriorated in response to progressive cerebral changes. The patient is presently in a vegetative state. We present this case to inform clinicians of association between lymphoma and immunodeficiency and explore an optimal treatment for lymphoma patients with compromised immune system.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".