Using Digital Multimedia to Learn the Human Gross Anatomy: A Virtual Guide to the Musculoskeletal System
Bibliographic record
Abstract
Objectives With the goal of improving students' gross anatomy learning experience, we created a visual, interactive presentation of gross anatomy of the gluteal and posterior thigh region. Our specific objectives were to provide students with some background knowledge prior to attending the lab, as well as allowing them to use the videos during the lab, and as a study resource after labs to enhance their learning and exam preparation. Methods The video discuss all the key anatomical structures of the gluteal and posterior thigh as defined by the first year medical undergraduate curriculum learning objectives. It also uses interactive labelling, commentary, and questions to enhance the student learning experience. Results Surveys were distributed amongst 290 medical students before and after they completed the lab to determine whether the videos were helpful. Over 95% of the students felt that the videos made them more prepared for the labs and enhanced their learning in the lab. They also endorsed the need for more videos for future gross anatomy labs. Conclusion Given the positive feedback, we conclude that this visual guide served to enhance the students' gross anatomy experience, and implementing more of such resources would help with their learning and understanding of the material. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.004 |
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".