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Record W2037842344 · doi:10.3138/jvme.33.3.465

An Electronic Instructor for Gross Anatomy Dissection

2006· article· en· W2037842344 on OpenAlexvenueno aff
Eleanor M. Josephson, L. J. Moore

Bibliographic record

VenueJournal of Veterinary Medical Education · 2006
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsnot available
FundersAuburn UniversityAdobe Systems
KeywordsDissection (medical)Gross anatomySignificant differenceMedicinePsychologyMedical educationSurgeryAnatomyInternal medicine

Abstract

fetched live from OpenAlex

Gross anatomy is time consuming to teach and to learn. Because the process of dissection takes up so much student time, assistance in the form of an in-lab instructional DVD program might improve student performance. The DVD could be viewed with a portable device by individual dissection groups at their tables. Groups could dissect at their own pace, with access to step-by-step demonstrations and answers to frequently asked anatomical questions. We created an instructional DVD program demonstrating dissection of the canine ventral neck and thoracic limb. The effect on student exam scores of using the DVD versus not using it was measured in a controlled, two-sample study using incoming first-year veterinary students as volunteers. Volunteers were told the study was of two different dissection methods; the DVD was not specifically mentioned until after the students were separated into two groups (Blue/DVD group and Orange/No DVD group), and then only to volunteers in the Blue group. Except for the DVD, the two groups had the same resources. The difference in scores on an exam given after a single dissection period did not differ sufficiently to conclude that DVD use raised the mean score; however, 73% of the DVD group scored 60% or higher, while only 38% of the No DVD group scored 60% or higher. The difference in mean scores overall was 2.3 points out of a possible 49, suggesting that the DVD helped students, especially those with lower scores, to earn two to three more points than they would have otherwise.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.287

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 head0.010
GPT teacher head0.326
Teacher spread0.316 · 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 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

Citations14
Published2006
Admission routes1
Has abstractyes

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