The role of Physiology in the development of a highly integrated clinically based curriculum at the Paul L. Foster School of Medicine
Bibliographic record
Abstract
The establishment of the new four year allopathic medical school in El Paso has provided the opportunity to develop an integrated organ based curriculum. This presentation describes the new curriculum and discusses the role of physiology. Here we focus on the first two years which are devoted to basic science delivery in the context of clinical presentations. Our El Paso curriculum is based on a model originally developed and proofed at The University of Calgary Medical School in Calgary Canada. There are 120 clinical presentations based on why a patient would seek medical attention. Each clinical presentation is illustrated by a scheme which represents the expert physicians approach to the patient's complaint. Following scheme presentation, faculty present the basic science needed to understand the decision tree presented in the scheme. The current faculty in El Paso consists of 15 PhD basic scientist representing the traditional disciplines and 4 MD clinical medical educators. Physiology represents 16 % of the contact time with a total of 185 hours of physiology including 60 hours of neurophysiology. According to the Chairs of Physiology report (2006), this emphasis on non‐neural physiology is 37% higher than the national average. While we teach 60 hours of neurophysiology compared to a national average of 9 hours. Physiology is playing a vital role in the overall development of this integrated curriculum.
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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 | 0.005 | 0.005 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".