Evidence map›Paper›PMID 29721456›Full record

ReviewMuscles, ligaments and tendons journal

Sensitivity analysis of the material properties of different soft-tissues: implications for a subject-specific knee arthroplasty.

Silvia Pianigiani, Davide Croce, Marta D'Aiuto, Walter Pascale, Bernardo Innocenti

Abstract readReview
In one paragraph

Review in Muscles, ligaments and tendons journal. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

5 authors.

Silvia PianigianiBEAMS Department, École polytechnique de Bruxelles, Université Libre de Bruxelles, Belgium.
Davide CroceDipartimento di Chimica, Materiali ed Ingegneria Chimica "G. Natta", Politecnico di Milano, Milan, Italy.
Marta D'AiutoDipartimento di Chimica, Materiali ed Ingegneria Chimica "G. Natta", Politecnico di Milano, Milan, Italy.
Walter PascaleIRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
Bernardo InnocentiBEAMS Department, École polytechnique de Bruxelles, Université Libre de Bruxelles, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionWhile developing a subject-specific knee model, different kinds of data-inputs are required. If information about geometries can be definitely obtained from images, more effort is necessary for the

methodsThis work aims at developing an intact native subject-specific knee model for performing a sensitivity analysis on soft-tissues. The impacts on the biomechanical outputs were analysed during a daily activity for which articular knee kinetics and kinematics were compared among the different configurations. Prior to the sensitivity analysis, experimental and literature data were checked for the model reliability.

resultsAverage values of mixed sources allowed the agreement with experimental data for personalized outputs. From the sensitivity analysis, knee kinematics did not significantly change in the selected ranges of properties for the soft-tissues (in rotation less than 0.5°), while contact stresses were greatly affected, especially for the articular cartilage (with differences in the results more than 100%).

conclusionIn conclusion, during the development of a personalized knee model, the selection of the correct material properties is fundamental because wrong values could highly affect the numerical results. LEVEL OF EVIDENCE: III a.

Indexed as

finite element analysisknee modelmaterial propertiessoft-tissuessubject-specific

Identifiers

PMID29721456
PMCPMC5908331

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.