Concerning the Use of Doppler and 3-D Ultrasound in the Teaching of Gross Anatomy in a New Curriculum Featuring the Use of Clinical Presentation Schemes
Bibliographic record
Abstract
Abstract Although gross anatomical instruction has always been considered a foundation for medical education and practice, curricular changes have altered the manner in which it is taught. The integration of basic biomedical sciences began with the case-western reserve curriculum. Under the leadership of Dr. Henry Mandin, the University of Calgary School of Medicine developed a program of medical instruction based on 120 clinical presentation schemes. The Paul L Foster School of Medicine is currently using this approach in teaching its first class of freshman medical students. Three-dimensional ultrasound and the other visual modalities—radiography, computerized tomography (CT) and magnetic resonance imaging (MRI)—have greatly increased our ability to visualize anatomical structures. Three-dimensional ultrasound has great potential for use in the curriculum at Paul L Foster School of Medicine. For example, it can help the student differentiate between the varieties of pelvic mass, which can be subdivided into ovarian, tubal, and uterine causes. This paper demonstrates the ways that Doppler threedimensional ultrasound can help the student differentiate between these anatomical diagnoses. Coupling ultrasound with the histopathologic study of various lesions can provide a powerful visual learning tool that mimics the use of these techniques in a clinical setting.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".