Evidence map›Paper›PMID 39294102›Full record

ArticleJournal of orthopaedic research : official publication of the Orthopaedic Research Society2025

Understanding the variability of the proximal femoral canal: A computational modeling study.

Angelika Ramesh, Johann Henckel, Alister Hart, Anna Di Laura

Abstract read
In one paragraph

Article in Journal of orthopaedic research : official publication of the Orthopaedic Research Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Quantifying the anatomical variability of the proximal femur.International journal of computer assisted radiology and surgery · 2026
    Article
  3. Current Uses for Medical Imaging With Orthopaedic Implant Technology.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2026
    Review
  4. Understanding the variability of the proximal femoral canal: A computational modeling study.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025
    Article
  5. Review
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

4 authors.

Angelika RameshDepartment of Mechanical Engineering, University College London, Gower Street, London, UK.ORCID 0000-0002-7396-4068
Johann HenckelRoyal National Orthopaedic Hospital NHS Trust, Brockley Hill, Stanmore, UK.
Alister HartRoyal National Orthopaedic Hospital NHS Trust, Brockley Hill, Stanmore, UK.
Anna Di LauraDepartment of Mechanical Engineering, University College London, Gower Street, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Statistical shape modeling (SSM) offers the potential to describe the morphological differences in similar shapes using a compact number of variables. Its application in orthopedics is rapidly growing. In this study, an SSM of the intramedullary canal of the proximal femur was built, with the aim to better understanding the complexity of its shape which may, in turn, enhance the preoperative planning of total hip arthroplasty (THA). This includes the prediction of the prosthetic femoral version (PFV) which is known to be highly variable amongst patients who have undergone THA. The model was built on three dimensional (3D) models of 64 femoral canals which were generated from pelvic computed tomography images including the proximal femur in the field of view. Principal component analysis (PCA) was performed on the mean shape derived from the model and each segmented canal. Five prominent modes of variations representing approximately 84% of the total 3D variations in the population of shapes were found to capture variability in size, proximal torsion, intramedullary femoral anteversion, varus/valgus orientation, and distal femoral shaft twist/torsion, respectively. It was established that the intramedullary femoral canal is highly variable in its size, shape, and orientation between different subjects. PCA-driven SSM is beneficial for identifying patterns and extracting valuable features of the femoral canal.

Indexed as

FemurAgedArthroplasty, Replacement, HipComputer SimulationFemaleHumansImaging, Three-DimensionalMaleMiddle AgedModels, AnatomicPrincipal Component AnalysisTomography, X-Ray Computedintramedullary femoral canalprincipal component analysisproximal femurstatistical shape modeling

Identifiers

PMID39294102
PMCPMC11615424

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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.