ArticleASAIO journal (American Society for Artificial Internal Organs : 1992)
Assessing Computational Model Credibility Using a Risk-Based Framework: Application to Hemolysis in Centrifugal Blood Pumps.
Article in ASAIO journal (American Society for Artificial Internal Organs : 1992). The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 27 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
27 citing papers in PubMed.
- Silicon Nitride Coatings on Titanium for Cardiovascular Applications: Interface Engineering, Hemocompatibility, and Translational Challenges.Materials (Basel, Switzerland) · 2026Review
- Post-MI Remodeling Mechanics of Left Ventricle: Microstructure-Informed Models, Identifiability, and Uncertainty for Patient-Specific Prediction.Bioengineering (Basel, Switzerland) · 2026Review
- Modeling and Optimal Input Design for Infra-Hepatic Blood Flow Regulation Systems.Bioengineering (Basel, Switzerland) · 2026Article
- Mechanical Performance of Thoracic Aortic Stent-Grafts: An In Vitro and In Silico Study.Annals of biomedical engineering · 2026Article
- From digital twins to clinically trustworthy twins: a clinical-claim-based validation framework for personalized digital health.Frontiers in digital health · 2026Article
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Review
- Credibility Assessment of the Patient-Specific Modeling of the Aneurysmal Ascending Thoracic Aorta: Verification, Validation and Uncertainty Quantification.Cardiovascular engineering and technology · 2025Article
- Verification, validation, and uncertainty quantification of finite element analysis results for pedicle screw assemblies under ASTM F1717 flexion and extension testing.Frontiers in bioengineering and biotechnology · 2025Article
- Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice.Journal of pharmacokinetics and pharmacodynamics · 2024Review
- Towards a comprehensive assessment of QSP models: what would it take?Journal of pharmacokinetics and pharmacodynamics · 2024Review
- Personalized prosthesis design in all-on-4® treatment through deep learning-accelerated structural optimization.Journal of dental sciences · 2024Article
- Credibility assessment of in silico clinical trials for medical devices.PLoS computational biology · 2024Review
- Relevant Choices Affecting the Fatigue Analysis of Ni-Ti Endovascular Devices.Materials (Basel, Switzerland) · 2023Review
- Recommendations for finite element modelling of nickel-titanium stents-Verification and validation activities.PloS one · 2023Article
- Design and execution of a verification, validation, and uncertainty quantification plan for a numerical model of left ventricular flow after LVAD implantation.PLoS computational biology · 2022Article
- Reliable Numerical Models of Nickel-Titanium Stents: How to Deduce the Specific Material Properties from Testing Real Devices.Annals of biomedical engineering · 2022Article
- Distribution and history of extensional stresses on vWF surrogate molecules in turbulent flow.Scientific reports · 2022Article
- Quantitative Systems Pharmacology Approaches for Immuno-Oncology: Adding Virtual Patients to the Development Paradigm.Clinical pharmacology and therapeutics · 2021Review
- In Silico Assessment of Class I Antiarrhythmic Drug Effects onInternational journal of molecular sciences · 2021Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Medical device manufacturers using computational modeling to support their device designs have traditionally been guided by internally developed modeling best practices. A lack of consensus on the evidentiary bar for model validation has hindered broader acceptance, particularly in regulatory areas. This has motivated the US Food and Drug Administration and the American Society of Mechanical Engineers (ASME), in partnership with medical device companies and software providers, to develop a structured approach for establishing the credibility of computational models for a specific use. Charged with this mission, the ASME V&V 40 Subcommittee on Verification and Validation (V&V) in Computational Modeling of Medical Devices developed a risk-informed credibility assessment framework; the main tenet of the framework is that the credibility requirements of a computational model should be commensurate with the risk associated with model use. This article provides an overview of the ASME V&V 40 standard and an example of the framework applied to a generic centrifugal blood pump, emphasizing how experimental evidence from in vitro testing can support computational modeling for device evaluation. Two different contexts of use for the same model are presented, which illustrate how model risk impacts the requirements on the V&V activities and outcomes.
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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.