Hemophagocytic Lymphohistiocytosis Secondary to Acute Human Immunodeficiency Virus and COVID-19
Bibliographic record
Abstract
Hemophagocytic lymphohistiocytosis (HLH), characterized by acute and progressive hyperinflammation, is a rare syndrome documented in a limited number of coronavirus disease 2019 (COVID-19) and human immunodeficiency virus (HIV) cases. While severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can provoke extensive immune activation and systemic inflammation, individuals with HIV, susceptible to immune dysregulation, are at heightened risk of severe complications from SARS-CoV-2. We report a case of a 24-year-old male with no significant medical history presenting with fever, weight loss, respiratory symptoms, and acute renal failure. Initial diagnosis revealed HIV with a CD4 count < 20 and concurrent COVID-19 infection leading to development of HLH. Despite aggressive management including antiretroviral therapy (ART), dexamethasone and supportive care, the patient deteriorated rapidly, leading to multiorgan failure. Coinfection with HIV and SARS-CoV-2 presents unique challenges, especially when complicated by secondary conditions such as HLH, which remains a diagnostic and therapeutic dilemma. Prompt recognition and aggressive management are crucial, necessitating a high index of suspicion and comprehensive evaluation including bone marrow biopsy to improve diagnostic accuracy and guide therapeutic interventions in such complex scenarios.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".