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A Comparison of Commercial Anatomy Educational Software

2012· article· en· W3177296249 on OpenAlexaff
Stefanie M. Attardi, Kem A. Rogers

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceCLARITYSoftwareComponent (thermodynamics)Gross anatomyRendering (computer graphics)MultimediaSoftware engineeringHuman–computer interactionAnatomyComputer graphics (images)MedicineBiology

Abstract

fetched live from OpenAlex

An online version of an existing undergraduate, systemic human anatomy course is under development. To accommodate the laboratory component of the online section, commercially available anatomy educational software will be used to demonstrate 3D structures via application sharing within a virtual classroom. Eleven anatomy software programs were reviewed to determine their suitability, on the basis of: quality of digital models (resolution, comprehensiveness of anatomical structures and labels, inclusion of cross sectional anatomy), volumetric data used to create models, manipulation of models (virtual dissection, rotational axes, vantage points, rendering speed), program functionality (saving and sharing dissections, querying for structures, ease of use of the menu) and cost. The software we reviewed will not meet all of our online teaching needs. No program was found to have sufficient anatomical detail and visual clarity of digital models for the central nervous system (CNS). Supplementary online materials for the CNS component of the course will be developed to use in conjunction with one of the eleven existing software packages. Grant Funding Source : Departmental Funding

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.546

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 integrity0.0000.000
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 head0.046
GPT teacher head0.387
Teacher spread0.342 · 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 designObservational
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

Citations1
Published2012
Admission routes1
Has abstractyes

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