ReviewSeminars in thrombosis and hemostasis2021
The Art and Science of Building a Computational Model to Understand Hemostasis.
Review in Seminars in thrombosis and hemostasis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- Mesoscale Simulations of Blood Coagulation in Flow and Quiescent Domains Using SDPD.International journal for numerical methods in biomedical engineering · 2026Article
- Protofibril packing density of individual fibers alters fibrinolysis.Research and practice in thrombosis and haemostasis · 2025Article
- Coagulo-Net: Enhancing the mathematical modeling of blood coagulation using physics-informed neural networks.Neural networks : the official journal of the International Neural Network Society · 2024Article
- A new look at TFPI inhibition of factor X activation.PLoS computational biology · 2024Article
- Decoding thrombosis through code: a review of computational models.Journal of thrombosis and haemostasis : JTH · 2024Review
- Physiological Modeling of Hemodynamic Responses to Sodium Nitroprusside.Journal of personalized medicine · 2023Article
- Clinical Predictors and Prediction Models for rFVIII-Fc Half Life in Real-World People with Severe Hemophilia A.Journal of clinical medicine · 2023Article
- Multiphysics and multiscale modeling of microthrombosis in COVID-19.PLoS computational biology · 2022Article
- Recent Advances in Computational Modeling of Biomechanics and Biorheology of Red Blood Cells in Diabetes.Biomimetics (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 5 institutions in 1 country.
Funding
Abstract
Computational models of various facets of hemostasis and thrombosis have increased substantially in the last decade. These models have the potential to make predictions that can uncover new mechanisms within the complex dynamics of thrombus formation. However, these predictions are only as good as the data and assumptions they are built upon, and therefore model building requires intimate coupling with experiments. The objective of this article is to guide the reader through how a computational model is built and how it can inform and be refined by experiments. This is accomplished by answering six questions facing the model builder: (1) Why make a model? (2) What kind of model should be built? (3) How is the model built? (4) Is the model a "good" model? (5) Do we believe the model? (6) Is the model useful? These questions are answered in the context of a model of thrombus formation that has been successfully applied to understanding the interplay between blood flow, platelet deposition, and coagulation and in identifying potential modifiers of thrombin generation in hemophilia A.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.