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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".