Evidence map›Paper›PMID 37644287›Full record

ArticleAdvances in experimental medicine and biology2023

An Overview of Traditional and Advanced Visualization Techniques Applied to Anatomical Instruction Involving Cadaveric Dissection.

Kenneth Hisley

Abstract read
PubMed Publisher
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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
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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

1 author at 1 institution in 1 country.

Kenneth HisleyPre-Clinical Studies - Anatomy, William Carey University College of Osteopathic Medicine, Hattiesburg, MS, USA. chisley@wmcarey.edu.
William Carey University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work provides an overview of the role, basic concepts, significance, and instructional contributions of current and evolving digital visualization technologies being applied in first-year anatomy curricula. These are visualization methods that have been and are being used to support both basic science and clinical applications of gross anatomical teaching and learning to the health professions (i.e., medical, dental, physical therapy, chiropractic and nursing students). It first presents a foundation as to how this discipline has been and is being taught within the professional school environment using visualization and illustration: aspects of learning, the format of the first-year anatomy curriculum, the nature of the visual information network in support of educational excellence and newer opportunities afforded by advanced technologies placing the student at the center of the learning experience. Then, the nature of each of these new methods is presented with their individual unique characteristics, and the results that anatomy faculty running cadaveric dissection courses had with the evaluation of the new technologies.The Conclusion section lists key points found in the literature as reported. Finally, the Future Work section proposes investigations into standardizing the presentation and assessment of anatomical concepts using prominent in situ structures of viscera, their enclosures and resident compartments for more precise and reproducible measurement of then instructional effectiveness of the new techniques.

Indexed as

LearningRelaxation TherapyCadaverCurriculumHumansStudentsAnatomy educationAugmented realityMedical educationVirtual realityVisualization techniques

Identifiers

PMID37644287
OpenAlexW4386263293

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.