MétaCan
Menu
Back to cohort

Evaluation of a Google Cardboard Stereo Image Viewer as a Valid Alternative to Cadaveric Specimens for Testing Anatomical Knowledge

2020· article· en· W3017322033 on OpenAlexaff
Alex B. Bak, Abigail Simms, David S. Shin, Sakshi Sinha, Josh Mitchell, Anthony N. Saraco, Danielle Brewer‐Deluce, Bruce Wainman

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCadaveric spasmStereoscopyModalitiesModality (human–computer interaction)Test (biology)CadaverMedicineComputer scienceArtificial intelligenceAnatomy

Abstract

fetched live from OpenAlex

The application of three‐dimensional (3D) visualization technology has generated interest due to its potential to augment or even replace cadaver use in anatomical education. By adopting stereoscopic 3D digital technologies, educational institutions may be able to mitigate issues related to the resource intensiveness of cadaver use such as prosection availability, body donation and the infrastructure to maintain physical specimens. We previously developed a smartphone application, titled VRBR, which uses an inexpensive Google Cardboard headset to display 2D or stereoscopic 3D images of cadaveric and plastinated specimens and plastic models for learning and testing anatomical knowledge. The purpose of this study was to compare the effectiveness and validity of stereoscopic 3D images, 2D images and cadaveric specimens in testing anatomical knowledge with practical examinations (OSPEs) for undergraduate students with prior anatomy education. As cadaveric specimens and stereoscopic 3D images are both stereoscopic, we hypothesized that participants would perform similarly between those modalities, and more poorly in 2D. Students who had previously completed an undergraduate anatomy and physiology course (N = 60) were randomized to one of three testing groups (A, B or C). Each testing group was administered OSPEs in three distinct modalities: VRBR 2D images, VRBR stereoscopic 3D images or cadaveric specimens. In total, each participant answered 45 anatomy‐based questions (15 per modality, test order randomized across groups) and completed a questionnaire assessing cybersickness and user satisfaction. Participants completed a stereo fly test and mental rotation test to assess their stereoacuity and visuospatial ability, respectively. These are potential covariates of the testing outcome when assessing spatially complex objects of varying depth. This study was approved by the Hamilton Integrated Research Ethics Board. In regard to cybersickness, a small fraction reported nausea (7.7%), vertigo (7.7%), and dizziness (15.4%) while responses for headache (23.1%) and fatigue (23.1%) were moderate and general discomfort (61.5%) and eyestrain (76.9%) were more prevalent. Preliminary data (N = 13) assessed via two‐way ANOVA, suggested that there were no statistically significant effects of test version (F(1, 22) = 0.005, p = 0.945) or stereoscopy (F(1, 22) = 0.009, p = 0.728). If this trend persisted through completion of the study, it may suggest that stereoscopy, as provided by the headset, does not improve the effectiveness of digital tools for testing anatomy knowledge recall. Complete data collection and analysis in February 2020 will provide a more complete picture of the validity of stereoscopy in testing anatomy knowledge recall. With ongoing analysis, the results from this study may elucidate the effect of stereopsis in testing anatomical knowledge and guide the future development of undergraduate anatomy education curricula. Support or Funding Information Funded by the Education Program in Anatomy.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.327
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicAnatomy and Medical TechnologyFrench-language works237,207