Evidence map›Paper›PMID 37892915›Full record

ArticleBioengineering (Basel, Switzerland)2023

Prediction of the Proximal Humerus Morphology Based on a Statistical Shape Model with Two Parameters: Comparison to Contralateral Registration Method.

Florianne E van Schaardenburgh, H Chien Nguyen, Joëll Magré, Koen Willemsen, Bert van Rietbergen, Stefaan Nijs

Abstract read
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Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

2 citing papers in PubMed.

  1. Accurate Humeral Version Angle Prediction From Limited Anatomical Data Using Statistical Shape Models.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026
    Article
  2. Shape-matching-based fracture reduction aid concept exemplified on the proximal humerus-a pilot study.International journal of computer assisted radiology and surgery · 2025
    Article
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

6 authors.

Florianne E van SchaardenburghOrthopaedic Biomechanics, Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
H Chien NguyenDepartment of Orthopaedics, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.ORCID 0000-0001-5436-7981
Joëll MagréDepartment of Orthopaedics, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.ORCID 0000-0003-1689-5104
Koen Willemsen3D Lab, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.ORCID 0000-0002-8237-6321
Bert van RietbergenOrthopaedic Biomechanics, Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands.
Stefaan NijsDivision Surgical Specialties, Department Trauma Surgery, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands.ORCID 0000-0002-1119-4918

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

(1) Background: Complex proximal humerus fractures often result in complications following surgical treatment. A better understanding of the full 3D displacement would provide insight into the fracture morphology. Repositioning of fracture elements is often conducted by using the contralateral side as a reconstruction template. However, this requires healthy contralateral anatomy. The purpose of this study was to create a Statistical Shape Model (SSM) and compare its effectiveness to the contralateral registration method for the prediction of the humeral proximal segment; (2) Methods: An SSM was created from 137 healthy humeri. A prediction for the proximal segment of the left humeri from eight healthy patients was made by combining the SSM with parameters. The predicted proximal segment was compared to the left proximal segment of the patients. Their left humerus was also compared to the contralateral (right) humerus; (3) Results: Eight modes explained 95% of the variation. Most deviations of the SSM prediction and the contralateral registration method were below the clinically relevant 2 mm distance threshold.; (4) Conclusions: An SSM combined with parameters is a suitable method to predict the proximal humeral segment when the contralateral CT scan is unavailable or the contralateral humerus is unhealthy, provided that the fracture pattern allows measurements of these parameters.

Indexed as

3D planninghumeral anatomyproximal humerus fracturesstatistical shape modelling

Identifiers

PMID37892915
PMCPMC10604326

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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.