Anatomical variations: How do surgical and radiology training programs teach and assess them in their training curricula?
Bibliographic record
Abstract
Sound knowledge of anatomy and Anatomical variations plays an integral role in surgical and radiology specialties. This study investigated the current teaching and assessment trends on Anatomical variations in various surgical and radiology specialty training curricula in Canada and Australia. A survey was sent to 122 Program Directors and Chairs of specialty committees in Canada and Directors of Training/Education in Australia of selected surgical and radiology specialties. A total of 80.7% of respondents report that their training curricula include Anatomical variations. The highest rated classes of variations included in the curriculum are arterial (76%), venous (68%), followed by organs (64%). All trainees learn about Anatomical variations from surgeons and radiologists (100%) and via suggested textbooks of the specialty (87.1%). A total of 54.8% report that specialty training curricula do not suggest specific anatomical variation classifications for the trainees to learn, and 16.1% are uncertain if the colleges provide such kind of instruction. Trainees typically communicated findings of variations in case presentations and clinic's meetings. About 32.3% of respondents report that Anatomical variations are not assessed in their training curriculum. About 39.3% of experienced clinicians in the study report they encounter variations on a monthly basis and 25 and 21.4% on a weekly and daily basis, respectively. Surgical and radiology colleges need to investigate for hidden curriculum in their specialty training programs to ensure there are no gaps in knowledge and training related to Anatomical variations. Most educational leaders surveyed believe more teaching on Anatomical variations in the first 4 years of training would benefit resident doctors.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".