Expanding application of the Wiggers diagram to teach cardiovascular physiology
Bibliographic record
Abstract
Dr. Carl Wiggers' careful observations have provided a meaningful resource for students to learn how the heart works. Throughout the many years from his initial reports, the Wiggers diagram has been used, in various degrees of complexity, as a fundamental tool for cardiovascular instruction. Often, the various electrical and mechanical plots are the novice learner's first exposure to simulated data. As the various temporal relationships throughout a heartbeat could simply be memorized, the challenge for the cardiovascular instructor is to engage the learner so the underlying mechanisms governing the changing electrical and mechanical events are truly understood. Based on experience, we suggest some additions to the Wiggers diagram that are not commonly used to enhance cardiovascular pedagogy. For example, these additions could be, but are not limited to, introducing the concept of energy waves and their role in influencing pressure and flow in health and disease. Also, integrating concepts of exercise physiology, and the differences in cardiac function and hemodynamics between an elite athlete and normal subject, can have a profound impact on student engagement. In describing the relationship between electrical and mechanical events, the instructor may find the introduction of premature ventricular contractions as a useful tool to further understanding of this important principle. It is our hope that these examples can aid cardiovascular instructors to engage their learners and promote fundamental understanding at the expense of simple memorization.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".