Essential Anatomy for Primary Care vs. Non‐Primary Care Clerkships and Electives
Bibliographic record
Abstract
Introduction To prepare medical students for clinical training and practice, it is critical to understand the anatomical knowledge considered most important for different clinical specialties. Aim To address this issue, a consortium of anatomists in the US and Canada is collecting data from clinical educators in clerkships and electives to identify the anatomy they consider essential for their specialty. Methods An IRB‐approved, online survey (Qualtrics, Seattle, WA) was used to assess the importance of 98 anatomical topics in seven body regions. The study first examined the nominal relationship between groups of primary care (Family Medicine, General Internal Medicine, General Pediatrics) and non‐primary care specialties, and whether they considered a given anatomical region important to their clerkship/elective. Second, the study compared the rank assigned by each specialty to each anatomical topic using an ordinal scale from 1 (not important) to 4 (essential). Results At the time of abstract submission, data had been collected from 598 clinicians (clerkship/elective directors and attending physicians) in 19 clerkships/electives at 33 medical schools (allopathic n=28; osteopathic n=5). Relative to Non‐Primary care physicians, Primary Care physicians showed a statistically significant, higher percentage of “Yes” responses for all seven anatomical regions (Table ). Further data analysis has identified the highest ranking anatomical topics within each region for Primary Care and Non‐Primary Care Specialties. Discussion and Conclusion This database provides detailed information regarding the most clinically relevant anatomical topics as identified by clinical educators. This information can aid in focusing preclinical learning to best prepare medical students for success in their undergraduate and graduate clinical experiences. Responses to the question: Is the anatomy of this body region important to your clinical specialty? Table 1
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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".