Concomitant Splenic Tuberculosis and Epstein–Barr Virus-Related T-Cell Leukemia/Lymphoma in a 28-Year-Old Pregnant Woman in South Sudan
Bibliographic record
Abstract
This case report presents a rare instance of concomitant splenic tuberculosis (TB), Epstein-Barr virus (EBV)-related T-cell leukemia/lymphoma, and malaria in a 28-year-old pregnant woman at a Médecins Sans Frontières-supported hospital in South Sudan. The patient was admitted with splenomegaly, anorexia, weakness, and transfusion-refractory anemia. She tested positive for malaria and was treated appropriately. Because of ongoing consumptive anemia, cachexia, and weakness severely impacting her quality of life, the patient underwent splenectomy. A diagnosis of TB was ultimately confirmed post-splenectomy through histopathological analysis and molecular testing. Gross findings from the pathologic analysis of a splenic sample revealed miliary deposits, necrotizing granulomas, and atypical lymphocytic infiltrates consistent with TB and EBV-associated leukemia/lymphoma. Despite temporary improvement post-operatively and the initiation of TB therapy, the patient discontinued treatment and was lost to follow-up, likely resulting in mortality. This report presents an unusual combination of concomitant pathologies that underscore the diagnostic challenges and complexity of managing overlapping infectious and hematological disorders in resource-limited settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".