A novel approach to physiology education for biomedical engineering students
Bibliographic record
Abstract
It is challenging for biomedical engineering programs to incorporate an indepth study of the systemic interdependence of cells, tissues, and organs into the rigorous mathematical curriculum that is the cornerstone of engineering education. To be sure, many biomedical engineering programs require their students to enroll in anatomy and physiology courses. Often, however, these courses tend to provide bulk information with only a modicum of live tissue experimentation. In the Electrical, Computer, and Biomedical Engineering Department of the University of Rhode Island, this issue is addressed to some extent by implementing an experiential physiology laboratory that addresses research in electrophysiology and biomechanics. The two-semester project-based course exposes the students to laboratory skills in dissection, instrumentation, and physiological measurements. In a novel approach to laboratory intensive learning, the course meets on six Sundays throughout the semester for an 8-h laboratory period. At the end of the course, students are required to prepare a two-page conference paper and submit the results to the Northeast Bioengineering Conference (NEBC) for consideration. Students then travel to the conference location to present their work. Since the inception of the course in the fall of 2003, we have collectively submitted 22 papers to the NEBC. This article will discuss the nature of the experimentation, the types of experiments performed, the goals of the course, and the metrics used to determine the success of the students and the research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.013 |
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".