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Record W4403514781 · doi:10.1080/03630269.2024.2371884

Venous Thromboembolism in Individuals with Sickle Cell Disease: A Narrative Review

2024· review· en· W4403514781 on OpenAlexaff
Ismail A. Raslan, Ziad Solh, Kevin H.M. Kuo, Jameel Abdulrehman

Bibliographic record

VenueHemoglobin · 2024
Typereview
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsWestern UniversityUniversity of Toronto
Fundersnot available
KeywordsNarrative reviewDiseaseMedicineVenous thromboembolismNarrativeCellIntensive care medicineInternal medicineGeneticsBiologyThrombosisPhilosophyLinguistics

Abstract

fetched live from OpenAlex

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.611
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.018
GPT teacher head0.317
Teacher spread0.299 · 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.

Study designSystematic review
Domainnot available
GenreReview

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

Citations6
Published2024
Admission routes1
Has abstractyes

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