Standardizing male pelvic anatomy teaching using a clinical enrichment video
Bibliographic record
Abstract
INTRODUCTION: Evolving trends in medical education and modern curricular changes have reduced the amount of time and resources allocated for anatomy education. As the amount of dedicated anatomy education time decreases, more self-directed learning is required. Cadaveric dissection and didactic teaching have been supplemented with multimedia, clinical anatomy, and imaging for over 20 years, with mixed results. Specifically, the use of video-based anatomy teaching increases medical learning, if done methodically. METHODS: A 20-minute video was produced highlighting surgical anatomy using the following operative cases: perineal anatomy (artificial urinary sphincter case), inguinal and testicular anatomy (scrotal orchidopexy for acute testicular torsion), prostate anatomy (robotic radical prostatectomy), and bladder anatomy (endoscopy). The annotated video was shown to first-year medical students. Pre- and post-video multiple choice question quizzes were given to the students. Once submitted, the students completed a survey. RESULTS: Overall, 191 first-year medical students participated in our study. Average scores were similar between each quiz (50±16% vs. 49±17%) and there was no statistically significant change. Seventy-seven percent of participants felt the video improved their knowledge of urologic anatomy and 83% agreed the video should be shown to future classes. Sixty percent of participants felt the video solidified their anatomy knowledge, 78% felt the video was stimulating and entertaining, and 43% of the students felt the video increased their interest in pursuing urology as a career choice. CONCLUSIONS: Anatomy teaching can be supplemented using surgical videos, especially in a time when in-person anatomy teaching is limited. Further study is required to determine whether this teaching modality improves long-term anatomy knowledge retention.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".