Dengue with Sickle Cell Anaemia Associated with Acute Liver Failure: A Case Report
Bibliographic record
Abstract
Aim: Dengue fever is a major health risk in endemic regions, and its complications can be severe in individuals with underlying haematological conditions like sickle cell anaemia (SCD). SCD increases the risk of Vaso-occlusive crises, haemolysis, and multi-organ dysfunction, which can be exacerbated by dengue’s endothelial damage. This case highlights the rare and complex interaction between SCD and dengue fever, particularly the severe hepatic complications due to co-existing illness, emphasizing the need for specialized treatment strategies. Case Presentation: A female patient from Chikkaballapur, Karnataka, India, with sickle cell anaemia (SCD), presented with dengue shock syndrome and severe hepatic complications. She experienced fever, shock, and liver dysfunction, worsened by both dengue and SCD. Diagnostic challenges arose due to overlapping symptoms. Management included careful hydration, pain control, and transfusion therapy, with close monitoring. Conclusions: This case demonstrates the complexities of managing patients with both SCD and dengue fever. The interaction between dengue-induced endothelial damage and SCD can lead to severe complications, including organ failure and death. Timely intervention, close monitoring, and a multidisciplinary approach are essential for improving outcomes. This case underscores the need for specific treatment guidelines for managing dengue in SCD patients to reduce morbidity and mortality.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| 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".