Comparison of Cardiac Auscultation Features on Four Different Simulation Mannequins Performed by Pediatric Residents
Bibliographic record
Abstract
INTRODUCTION: Cardiac murmurs are a common problem in pediatric clinical practice. Studies demonstrated low accuracy in detecting and diagnosing various cardiac murmurs at all levels of medical training. So, supplementary training methods started to evolve, including simulation for auscultation skills training. Over the years, mannequins have evolved with different types of technology. Therefore, we decided to compare cardiac auscultation accuracy among high-fidelity mannequins as the primary objective and compare the performance of various postgraduate-level residents as a secondary objective. METHOD: Pediatric residents at King Abdulaziz University Hospital were given a lecture on the basics of cardiac auscultation and then requested to auscultate four mannequins, namely SimJumior® (Laerdal Medical, Stavanger, Norway), SimBaby™ (Laerdal Medical), Pediatric HAL® (Gaumard Scientific, Miami, FL, USA), and Cardiac Patient Simulator K-Plus (Kyoto Kagaku Co. Ltd., Kyoto, Japan). The accuracies of murmur type, diagnosis, and auscultation time were compared. Results: A total of 56 pediatric residents were enrolled. Median murmur accuracy ranged from 50% to 53% (p-value 0.79), and median diagnosis accuracy ranged from 33% to 36% (p-value 0.77), with a nonsignificant difference between mannequins. Comparing resident levels in all mannequins, median murmur accuracy ranged from 49% to 56% (p-value 0.70), and median diagnosis accuracy ranged from 29% to 41% (p-value 0.09). While the median average auscultation time was between 41 and 50 seconds (p-value 0.34). CONCLUSION: Auscultation skills can be taught through simulation on any mannequin used in this comparison, not necessarily the cardiac one. For better accuracy, future comparisons might include more advanced cardiac mannequins based on cardiac auscultation expertise (i.e., consultant level). The introduction of an auscultation program from the undergraduate level throughout the training process and monitoring of these skills are mandated.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".