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Three Dimensional (3D) Virtual Human Cadaver, Anatomage: An effective teaching tool for Human Anatomy

2017· article· en· W3200296584 on OpenAlexaff
Yuwaraj Narnaware

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsMacEwan University
Fundersnot available
KeywordsHuman anatomyHuman bodyCadaverClass (philosophy)AnatomyVirtual actorComputer scienceHuman–computer interactionVirtual realityMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

In an attempt to explore the innovative ways to introduce the knowledge of the human body in terms of its structures and functions, we have first time introduced three dimensional (3D) virtual human cadaver, Anatomage in our human anatomy classes, and discuss whether the use of Anatomage enhances the understanding/knowledge of the human body as determined by their class average with or without using anatomage. We have observed that the use of Anatomage in their teaching significantly increase their class average. Moreover, we also discuss the limitations and advantages of Anatomage as an effective teaching tool to teach human 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.288
Teacher spread0.276 · 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 designOther design
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

Citations2
Published2017
Admission routes1
Has abstractyes

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