Evidence map›Paper›PMID 40986373›Full record

ArticleJournal of biomechanical engineering2026

Theoretical Considerations for Patient-Specific Modeling Based on Observable State Variables.

Gerard A Ateshian, Sarah Deiters, Jeffrey A Weiss

Abstract read
In one paragraph

Article in Journal of biomechanical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Gerard A AteshianDepartment of Mechanical Engineering, Columbia University, New York, NY 10027; Department of Biomedical Engineering, Columbia University, New York, NY 10027.
Sarah DeitersDepartment of Biomedical Engineering, Columbia University, New York, NY 10027.
Jeffrey A WeissDepartment of Biomedical Engineering and Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112.ORCID 0000-0002-7264-2454

Funding

Finite Elements For Biomechanics And BiophysicsR01GM083925 · NIGMS · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI GERARD A. ATESHIAN, JEFFREY A. WEISS · 2008 to 2026
$7.6M
NIGMS NIH HHS R01 GM083925
6 · The paper itself

Abstract

In this study, we address fundamental theoretical considerations that should guide biomedical engineers in the assessment of patient-specific risk of tissue failure, or the assessment of other material properties needed for patient-specific computational modeling, based on noninvasive imaging modalities. Upon reviewing theoretical concepts of mechanics, the primary conclusion is that patient-specific material properties, such as measures of tissue failure, cannot be observed directly, because material properties are dependent on nonobservable functions of state. However, since functions of state may be formulated to depend on observable state variables, and since noninvasive imaging may be used to assess such variables, it behooves investigators to find strong correlations in vitro between the material property of interest and relevant observable state variables, such as measures of tissue morphology, transport characteristics, and composition. Once such univariate or multivariate correlations have been established experimentally in vitro, the next challenge is to relate imaging-based observable measures, acquired noninvasively (e.g., in vivo), to relevant material properties such as failure criteria. The uncertainty associated with these observation-derived material properties is, at best, equal to the uncertainty of the in vitro correlation.

Indexed as

Mechanical PhenomenaPatient-Specific ModelingBiomechanical PhenomenaHumans

Identifiers

PMID40986373
PMCPMC12755162

What OpenQuestion holds

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Registered trials

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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.