An Electronic Instructor for Gross Anatomy Dissection
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.555 | 0.261 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".