Essential Anatomy for Neurology and Psychiatry in the Undergraduate Medical Curriculum
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 Neurology and Psychiatry clerkships and electives to identify the anatomy they consider essential. 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 percentage of Neurology and Psychiatry clinical educators (clerkship/elective directors and attending physicians) that considered each anatomical region important to their specialty. Second, the study examined the rank assigned 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 39 Neurology clinical educators at 11 medical schools and 19 Psychiatry clinical educators at 9 medical schools. The percentage of clinical educators that considered each anatomical region important to their specialty were (highest‐to‐lowest): Neurology – Back (97%), Upper Limb (97%), Lower Limb (97%), Head & Neck (97%), Thorax (54%), Pelvis & Perineum (46%), and Abdomen (31%); Psychiatry – Head & Neck (89%), Back (47%), Abdomen (37%), Thorax (32%), Upper Limb (26%), Lower Limb (26%), Pelvis & Perineum (16%). Further data analysis has identified the highest ranked anatomical topics in each body region for the Neurology and Psychiatry clerkships/electives. Discussion and Conclusion . This database provides detailed information regarding the most clinically relevant anatomical topics as identified by Neurology and Psychiatry clinical educators. This information can aid in focusing preclinical learning to best prepare medical students for success in their undergraduate and graduate clinical experiences.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".