The Development and Assessment of a Medical Education Resource that uses Surface Anatomy to create a link between gross anatomy and clinical skills
Bibliographic record
Abstract
As physicians use living anatomy in clinical practice, it is essential for medical students to gain similar experiences by studying surface anatomy. Using observation and palpation to identify surface landmarks for structures beneath the skin, there was an opportunity to refine skills required for clinical examination. A Surface Anatomy Resource was created to link foundational knowledge learned in gross anatomy with clinical skills. An in‐class workshop was developed to teach medical students techniques for identifying surface landmarks of anatomical structures, followed by an interactive, online module with questions that enhanced their skills. Diagnostic Quizzes were completed at the beginning and end of the study, and were used to assess the resource's effectiveness. Evidence that the Surface Anatomy resource improved Post Quiz scores was highly significant (p value = .0049), suggesting that the resource provided students with an opportunity to augment clinical examination skills. The greatest improvements in post quiz scores were found for students in years 2 and 3 of undergraduate medicine, suggesting that this resource served as a valuable review for upper‐year medical students in particular. This resource allowed participants to review and apply gross anatomy in their clinical skill development; quiz scores and positive feedback reflected its effectiveness.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".