Coagulopathy of Dengue and COVID-19: Clinical Considerations
Bibliographic record
Abstract
Thrombocytopenia and platelet dysfunction commonly occur in both dengue and COVID-19 and are related to clinical outcomes. Coagulation and fibrinolytic pathways are activated during an acute dengue infection, and endothelial dysfunction is observed in severe dengue. On the other hand, COVID-19 is characterised by a high prevalence of thrombotic complications, where bleeding is rare and occurs only in advanced stages of critical illness; here thrombin is the central mediator that activates endothelial cells, and elicits a pro-inflammatory reaction followed by platelet aggregation. Serological cross-reactivity may occur between COVID-19 and dengue infection. An important management aspect of COVID-19-induced immunothrombosis associated with thrombocytopenia is anticoagulation with or without aspirin. In contrast, the use of aspirin, nonsteroidal anti-inflammatory drugs and anticoagulants is contraindicated in dengue. Mild to moderate dengue infections are treated with supportive therapy and paracetamol for fever. Severe infection such as dengue haemorrhagic fever and dengue shock syndrome often require escalation to higher levels of support in a critical care facility. The role of therapeutic platelet transfusion is equivocal and should not be routinely used in patients with dengue with thrombocytopaenia and mild bleeding. The use of prophylactic platelet transfusion in dengue fever has strained financial and healthcare systems in endemic areas, together with risks of transfusion-transmitted infections in low- and middle-income countries. There is a clear research gap in the management of dengue with significant bleeding.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".