Venous Thromboembolism in Individuals with Sickle Cell Disease: A Narrative Review
Bibliographic record
Abstract
Sickle cell disease (SCD) is an inherited hemoglobinopathy characterized by vaso-occlusion, hemolysis of red blood cells (RBC), and a predisposition for venous thromboembolism (VTE). The sickling and hemolysis of RBC culminate in coagulation system abnormalities, platelet activation, endothelial dysfunction, and impaired blood flow manifesting as a prothrombotic state. In addition, individuals with SCD are often exposed to extrinsic risk factors for VTE including recurrent hospitalizations, central venous catheters, and acute medical illnesses. The diagnosis is often challenging as symptoms may mimic other complications of SCD, and there is little data to guide diagnostic algorithms involving probability scoring in the SCD population. Non-anticoagulant strategies aimed at reducing disease severity may aid in lowering the risk of VTE, but data is limited. Furthermore, high quality evidence regarding anticoagulation in prevention and treatment of SCD is severely lacking, resulting in heterogeneity in clinical practice. In this narrative review we aim to review the prothrombotic pathophysiology of SCD, to describe the risk factors, high risk of mortality, and types of VTE in SCD, to develop an approach to the diagnosis of VTE in SCD, and to understand the limited available evidence for the prevention and treatment of VTE in SCD.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".