Instruction Using a High-Fidelity Cardiopulmonary Simulator Improves Examination Skills and Resource Allocation in Family Medicine Trainees
Bibliographic record
Abstract
INTRODUCTION: High-fidelity cardiopulmonary simulators have proven promising in various areas of medical education but have yet to be studied in Family Medicine training. METHODS: A 2-hour curriculum, combining didactic and simulator exposure, and addressing common valvular pathologies, was offered to post-graduate year 1 and 2 Family Medicine residents. Residents' abilities to describe and diagnose four simulated murmurs were assessed before the teaching sessions and 2 to 4 weeks after. Confidence in physical examination skills, as well as the use of echocardiography, was also measured. RESULTS: Twenty residents participated. Mean composite murmur description scores improved in 95% of residents (P < 0.001), as did mean diagnostic accuracy (from 43.8% to 85.0%; P < 0.001). For pathologic murmurs, the number of echocardiograms recommended did not change, whereas for the nonpathologic murmur, 16 residents who recommended echocardiography presession no longer did postsession (P < 0.001). Mean confidence significantly increased (P < 0.001). The mean satisfaction score for the session was 4.9/5, and all residents recommended that the session be repeated in future years. CONCLUSION: A didactic and simulator-based session is very well received by Family Medicine residents. It significantly improves description and diagnosis of murmurs and reduces unnecessary echocardiogram use without affecting appropriate use.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".