Integrating Anatomical Education into a Residency Curriculum of a Surgical Specialty
Bibliographic record
Abstract
Anatomy is a cornerstone of modern medical education yet there is a continuing trend toward decreasing the time devoted to cadaveric dissection during medical school. Has the decrease in its use negatively impacted student's anatomical foundation? This is an especially relevant question for postgraduate education in surgical specialties, where an understanding of the pertinent anatomy is vital. Medical students entering residency training in obstetrics and gynecology often do not have an adequate understanding of pelvic anatomy. In fact, the recent growth in professional development courses employing cadaveric dissections is a testament to practicing gynaecologists recognition of inadequate teaching of pelvic anatomy. Designing a program to address this knowledge deficit is a worthwhile aim, especially if it includes elements that will maximize the utility of anatomical knowledge in surgical practice. Learners need a three‐dimensional understanding of pelvic anatomy that is transferable to pelvic surgery. Such a program should have clinical correlations, and develop production sets that are foundational to gynaecologic procedures. Lastly, repetition is essential to knowledge retention. A multimodality approach to teaching surgical anatomy is the most practical means to achieve these pedagogical goals. Clay modeling is a three‐dimensional technique that serves as an adjunct to lectures to refresh anatomy knowledge in residents entering an Obstetrics and Gynaecology program. The objective of the clay‐modeling session is to allow residents to conceptualize and build the anatomic structures of the pelvis in 3 dimensions using pre‐cut clay structures and a bony pelvis. While the lectures and supplementary teaching methods, are useful to build on the foundation provided by the clay‐modeling lab, the value of cadaveric dissection cannot be overstated. For Gynaecology, both vaginal and laparoscopic cadaver labs should be included, and can be achieved in sequence on the same cadaver. Repetition is the key to sustaining long‐term learning. Perhaps the best way to achieve this is by creating a clinical learning environment that highlights the importance of anatomical considerations and creates opportunities for cognitive reinforcement. Ultimately, the most important approach is to define it as a core competency required for advancement, and to develop discriminating means to test for it. As postgraduate accreditation bodies increasingly adopt competency‐based curricula, the need for valid methods of assessing anatomical competency becomes more pressing.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".