ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2019
Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2019. 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, 4 citations in OpenAlex.
- Physics-Informed Emulation of Systemic Circulation for Fast Parameter Estimation and Uncertainty Quantification.International journal for numerical methods in biomedical engineering · 2026Article
- Multitask deep learning for the emulation and calibration of an agent-based malaria transmission model.PLoS computational biology · 2025Article
- Guidelines for mechanistic modeling and analysis in cardiovascular research.American journal of physiology. Heart and circulatory physiology · 2024Review
- Bridging the gap between mechanistic biological models and machine learning surrogates.PLoS computational biology · 2023Review
- Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics.Biomechanics and modeling in mechanobiology · 2022Article
- Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle.International journal for numerical methods in biomedical engineering · 2022Article
- Uncertainty Quantification of Regional Cardiac Tissue Properties in Arrhythmogenic Cardiomyopathy Using Adaptive Multiple Importance Sampling.Frontiers in physiology · 2021Article
- Efficient Ventricular Parameter Estimation Using AI-Surrogate Models.Frontiers in physiology · 2021Article
- A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion.Frontiers in bioengineering and biotechnology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
A central problem in biomechanical studies of personalized human left ventricular modelling is estimating the material properties and biophysical parameters from
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.