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Record W2085306395 · doi:10.3138/jvme.0512.037r

The Effectiveness of 3D Animations to Enhance Understanding of Cranial Cruciate Ligament Rupture

2012· article· en· W2085306395 on OpenAlexvenueno aff
Dylan N. Clements, Henry Broadhurst, Stephen P. Clarke, Michael Farrell, David Bennett, John R. Mosley, Richard J. Mellanby

Bibliographic record

VenueJournal of Veterinary Medical Education · 2012
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
FundersPetplan Charitable Trust
KeywordsPresentation (obstetrics)Cruciate ligamentMedicineAnimationPsychological interventionAnterior cruciate ligamentSurgeryComputer science

Abstract

fetched live from OpenAlex

Cranial cruciate ligament (CCL) rupture is one of the most important orthopedic diseases taught to veterinary undergraduates. The complexity of the anatomy of the canine stifle joint combined with the plethora of different surgical interventions available for the treatment of the disease means that undergraduate veterinary students often have a poor understanding of the pathophysiology and treatment of CCL rupture. We designed, developed, and tested a three dimensional (3D) animation to illustrate the pertinent clinical anatomy of the stifle joint, the effects of CCL rupture, and the mechanisms by which different surgical techniques can stabilize the joint with CCL rupture. When compared with a non-animated 3D presentation, students' short-term retention of functional anatomy improved although they could not impart a better explanation of how different surgical techniques worked. More students found the animation useful than those who viewed a comparable non-animated 3D presentation. Multiple peer-review testing is required to maximize the usefulness of 3D animations during development. Free and open access to such tools should improve student learning and client understanding through wide-spread uptake and use.

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)0.003
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 score0.801
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.095
GPT teacher head0.422
Teacher spread0.326 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

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