TEACHING ANATOMY AND PHYSIOLOGY TO ENGINEERS IN THE BIOMEDICAL ENGINEERING PROGRAM
Bibliographic record
Abstract
The merger of natural sciences with engineering and creation of biomedical engineering (BME) has brought innovation to the practice of medicine that could only be dreamed about a decade ago. By many accounts, we are now at the outset of Biomedical Century, and the need for engineers, natural scientists and physicians trained in biomedicine is greater than ever. While several universities in US and Canada have started BME program at the undergraduate level, many universities, particularly in Canada, have the BME program at graduate level with some BME related courses at undergraduate level. Therefore, we must be able to teach the fundamentals of human anatomy and physiology to students with engineering background, whom may have no background in biology.While a simple solution might be to refer students to take anatomy courses in the faculty of medicine, the author of this paper believes these courses would be much more efficient if they are taught by an engineer trained in medicine. There are three main approaches in engineering design: top-down and bottom-up and a blend of the two approaches [1-3]. There are many debates as which approach is most efficient in a particular area of science and philosophy. we believe engineers often use top-bottom approach for learning new concepts; through their main discipline, engineers, in general, learn the problem solving skills, in which they try to break a complex problem into several smaller problems and narrow down logically by cause and effect analysis; in another word they follow a top-down approach. Being trained with this approach, therefore, they may feel lost in a basic anatomy or physiology course as they often have a bottom-up approach in teaching the materials. This paper discusses the top-down approach in teaching anatomy and physiology to engineering students, and offers some insights for teaching BME courses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".