Evidence map›Paper›PMID 29243252›Full record

ArticleJournal of anatomy2018

Predicting calvarial growth in normal and craniosynostotic mice using a computational approach.

Arsalan Marghoub, Joseph Libby, Christian Babbs, Erwin Pauws, Michael J Fagan, Mehran Moazen

Open access · bronzeAbstract read
In one paragraph

Article in Journal of anatomy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.8field-weighted citation impact, top 13% of its field
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

15 citing papers in PubMed, 24 citations in OpenAlex.

  1. Article
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  5. Article
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  7. Article
  8. Review
  9. Mouse embryo phenotyping using X-ray microCT.Frontiers in cell and developmental biology · 2022
    Review
  10. Article
  11. Article
  12. Article
  13. Article
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  15. 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

6 authors at 4 institutions in 1 country.

Arsalan MarghoubDepartment of Mechanical Engineering, University College London, London, UK.
Joseph LibbyMedical and Biological Engineering, School of Engineering and Computer Science, University of Hull, Hull, UK.
Christian BabbsMRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
Erwin PauwsInstitute of Child Health, Great Ormond Street, University College London, London, UK.
Michael J FaganMedical and Biological Engineering, School of Engineering and Computer Science, University of Hull, Hull, UK.
Mehran MoazenDepartment of Mechanical Engineering, University College London, London, UK.ORCID 0000-0002-9951-2975
University College London · GBUniversity of Hull · GBGreat Ormond Street Hospital · GBUniversity of Oxford · GB

Funding

Biotechnology and Biological Sciences Research Council BB/E014259/1Biotechnology and Biological Sciences Research Council BB/H011668/1
6 · The paper itself

Abstract

During postnatal calvarial growth the brain grows gradually and the overlying bones and sutures accommodate that growth until the later juvenile stages. The whole process is coordinated through a complex series of biological, chemical and perhaps mechanical signals between various elements of the craniofacial system. The aim of this study was to investigate to what extent a computational model can accurately predict the calvarial growth in wild-type (WT) and mutant type (MT) Fgfr2

Indexed as

Computer SimulationAnimalsCraniosynostosesFinite Element AnalysisImage Processing, Computer-AssistedMiceSkullX-Ray Microtomographybiomechanicscalvarial bonescraniosynostosisdevelopmentfinite element methodsutures

Identifiers

PMID29243252
PMCPMC5807955
OpenAlexW2773952582

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.