Evidence map›Paper›PMID 37524984›Full record

ArticleAdvances in experimental medicine and biology2023

Evaluating a Photogrammetry-Based Video for Undergraduate Anatomy Education.

Irene Gianotto, Alexander Coutts, Laura Pérez-Pachón, Flora Gröning

Abstract read
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In one paragraph

Article in Advances in experimental medicine and biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Irene GianottoSchool of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.
Alexander CouttsSchool of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.
Laura Pérez-PachónSchool of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK.ORCID https://orcid.org/0000-0002-8014-6565
Flora GröningSchool of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK. f.groening@abdn.ac.uk.ORCID https://orcid.org/0000-0002-8127-4112

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modern anatomy education has benefitted from the development of a wide range of digital 3D resources in the past decades, but the impact of the COVID-19 pandemic has sparked an additional demand for high-quality online learning resources. Photogrammetry provides a low-cost technique for departments to create their own photo-realistic 3D models of cadaveric specimens. However, to ensure accessibility, the design of the resulting learning resources should be carefully considered. We aimed to address this by creating a video based on a photogrammetry model of a cadaveric human lung. Students evaluated three different versions of this video in a Likert-type online survey. Most responding students found this type of video useful for their learning and helpful for the identification of anatomical structures in real cadaveric specimens. Respondents also showed a preference for specific design features such as a short video length, white text on black background, and the presence of captions. The positive student feedback is promising for the future development of photogrammetry-based videos for anatomy education and this study has provided pilot data to improve the accessibility of such videos.

Indexed as

AnatomyEducation, Medical, UndergraduatePhotogrammetryVideo RecordingCadaverCOVID-19Education, DistanceHumansImaging, Three-DimensionalLung3D photogrammetryAccessibilityBlended learningGross anatomy educationLearning disabilitiesVideo creation

Identifiers

PMID37524984

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.