Outcomes of Using Heart Sound Simulator in Teaching Cardiac Auscultation
Bibliographic record
Abstract
Background - Despite continued curriculum reform, the clinical skills competencies of medical graduates at all levels are steadily declining within a training system, where bedside opportunities become a luxury and the laboratory tests prevail over the clinical skills. While high-fidelity expensive simulators are being embraced by high-procedure volume specialties, low-fidelity and relatively inexpensive simulators, such as the heart sounds simulators remain under-utilized in medical training. Methods - We used a commercially available heart sound synthesizer in 2-hour training sessions with students and residents. Pre-post testing was completed at the beginning of the training session and three weeks after the session; participant responses were recorded by audience interactive response system. Results - Data analysis was accomplished with statistical software SPSS 17.0 utilizing paired t-test, and showed a statistically significant difference in learners’ knowledge and skills attainment 3 weeks after completion of the simulation training (p<0.0001). Conclusions - Based on our findings and the review of literature, we recommend that heart sound simulation is introduced at medical student level as the standard for teaching cardiologic auscultation findings and as preparation for auscultation of live patients. We also suggest that training with digitally simulated heart sounds is similarly beneficial in resident training.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".