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Virtual Dissection Adds Educational Value to a Traditional Medical Undergraduate Cadaveric Anatomy Course

2018· article· en· W3175285794 on OpenAlexaff
Kathryn E. Darras, Rebecca Spouge, Abigail Arnold, Anique B. H. de Bruin, Savvas Nicolaou, Claudia Krebs, Rose Hatala, Bruce Forster

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCurriculumDissection (medical)Medical educationVirtual microscopyMedical physicsMedicineRadiologyGross anatomyPsychologyComputer scienceAnatomyPathologyPedagogy

Abstract

fetched live from OpenAlex

Background Virtual dissection is performed on near life‐size anatomy visualization tables (AVTs), which are like hospital radiology workstations. Patient CT scans are loaded into these tables and through powerful software interactions students work together to manipulate the data and perform their dissection. The purpose of this study was to develop virtual dissection laboratories for first year medical students and to qualitatively assess the educational value of these sessions as well as students' preferred pedagogical approaches for this new technology. Methods All students in the first‐year medical undergraduate program were included in this study (n = 292). A Basic Virtual Dissection Curriculum focused on normal anatomy was offered to all students concurrently with the Cadaveric Laboratory Sessions and an Advanced Virtual Dissection Curriculum focused on pathology was offered as 4 extra‐curricular sessions. 36.6% of students participated in the Advanced Curriculum. Following the course, both groups of students were surveyed to determine their attitude toward virtual dissection and the pedagogical approaches they perceived to be the most useful for this technology. Results were statistically analyzed using the Schulze method. Results The response rate for the Basic Curriculum was 69.2% and the response rate for the Advanced Curriculum was 82.9%. 93% indicated that virtual dissection was “definitely” a valuable addition to the anatomy lab. 89% of respondents “agreed” or “strongly agreed” that AVT virtual dissection improved their understanding of disease and pathology. They reported that the aortic aneurysm case was the most memorable case because the imaging made it easier to understand the pathogenesis of the disease. Students felt that small group demonstration and problem‐based learning would be the best teaching approaches for this technology. Conclusions Virtual dissection adds educational value to undergraduate anatomy teaching, primarily because it provides students with a clinical context for the anatomy they are learning. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.259
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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