Integrating Clinical Medicine with the Basic Sciences; Pulmonology Learning Modules for Medical Students
Bibliographic record
Abstract
Many medical students find memorizing the detailed of their basic science courses difficult and daunting, especially if they do not know what the application of that subject is during their medical practice. The overall objective of this study was to encourage students to adopt a more anatomical approach to clinical skills and also to show explicitly the relevance of pulmonary anatomy and embryology, thus moving away from rote memorization of physical exam skills and anatomical knowledge. In order to accomplish the goal of interactive case‐based modules we used the Articulate Engage software suite to create two modules that progress slide by slide in order to encourage a logical flow from patient presentation through to treatment. Using Articulate we were also able to add interactive labeled diagrams to review pulmonary anatomy, pictures to study clinical skills, and integrated quizzes to test student knowledge. The pulmonary modules were evaluated objectively from the survey questionnaire results and subjectively from the comments from the results. Before the modules 20% of students felt they knew the pulmonary materials well or very well. After the modules 58% of students felt they knew the pulmonary materials well or very well. Over 90% of students surveyed strongly or very strongly wanted to see more modules like these modules in their program.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".