Cadaveric versus Radiology Anatomy: Do we have the right balance to prepare medical students to be physicians?
Bibliographic record
Abstract
Background Radiology is one of the cornerstones of modern medicine, playing an important role in patient diagnosis and management. All physicians, regardless of their speciality, are now expected to provide preliminary interpretations of radiological studies throughout their careers. Many medical undergraduate anatomy programs are beginning to teach radiological concepts through their anatomy curricula to improve the clinical relevance of teaching and to better prepare students for clinical practice. However, there has been no research to date assessing the optimal balance between cadaveric and radiological anatomy. The purpose of this study is to assess medical students' comfort level in identifying normal cadaveric and radiological anatomy and to identify areas where curricula can be renewed. Methods An anonymous online survey was administered to the second, third and fourth year undergraduate medical students at a large distributed medical school which teaches both cadaveric and radiologic anatomy (N = 850). The survey collected respondents' demographic information as well as prior radiology exposure. Students were asked to rank their comfort level in identifying anatomy in cadavers and on radiology studies on a 4‐point Likert scale. Statistical analysis was carried out to determine if there was any difference in student responses based on year of training (i.e. pre‐clinical vs clinical) and the Copeland score method was used to generate a rank order list of student comfort level across all variables. Results 153 students completed the survey yielding a response rate of 18%. 50.3% of respondents were female and most respondents were between 18–24 years old. 51.6% of respondents were in their clinical years (i.e. year 3 and 4) and 48.4% were in their pre‐clinical years. Most (90.8%) students reported receiving no radiology teaching prior to medical school. Most students (65.4%) reported their overall anatomy knowledge as “average.” When Likert data was used to generate a rank‐order list, students reported feeling most comfortable when identifying cadaveric organ anatomy, cadaveric bony anatomy and recognizing spatial relationships in cadavers and least comfortable when identifying radiological pathology, radiological neuroanatomy, and recognizing spatial relationships on imaging studies. There was no statistical difference in response when considering students' level of training. Conclusions Overall, medical students reported feeling less comfortable when identifying normal anatomy on radiological studies when compared to identifying the same structures in the cadaver. This study suggests medical students would benefit from increased exposure to radiology anatomy during medical school, especially given that most physicians will be primarily examining radiology images throughout their careers. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".