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Record W4409525650 · doi:10.4269/ajtmh.25-0033

Concomitant Splenic Tuberculosis and Epstein–Barr Virus-Related T-Cell Leukemia/Lymphoma in a 28-Year-Old Pregnant Woman in South Sudan

2025· article· en· W4409525650 on OpenAlexaff
Hannah Wild, Joseph Aumuller, Joseph Kuei, Anna Palm, Jacob Pendergrast, J P Letoquart, Jefferson Terry, Shahrzad Joharifard

Bibliographic record

VenueAmerican Journal of Tropical Medicine and Hygiene · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Toronto
FundersFogarty International Center
KeywordsConcomitantLymphomaTuberculosisMedicineVirologyVirusLeukemiaEpstein–Barr virusPregnancyImmunologyPathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.227
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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