Relative effectiveness of high‐ versus low‐fidelity simulation in learning heart sounds
Bibliographic record
Abstract
CONTEXT: Although there are increasing numbers of studies of outcomes of high-fidelity patient simulators, few contrast their instruction with that provided by equivalent low-fidelity, inexpensive simulators. Further, examination of decays in learning and application (transfer) to real patient problems is rare. In this study, we compared the effects of training using a high-fidelity heart sound simulator (Harvey) and a low-fidelity simulator (a CD) on recognition of both simulated heart sounds and those in actual patients. METHODS: A pilot study with 10 students was conducted to show the feasibility of the methods and some evidence of modality-specific learning (the Harvey-trained group scored 72% correct on Harvey and 36% correct on CD test examples; the CD-trained group scored 60% correct on both CD and Harvey test examples). A main study was then initiated involving 37 Year 3 medical students from the University of Leeds. They received 1 hour of common instruction, after which one group received 3 hours of specific instruction on Harvey. The second group received 3 hours of instruction using a CD. Six weeks later, both groups were tested blind with real patients with stable heart sounds. Stations were observed by an examiner who scored communication skills and examination skills using 5-point scales. RESULTS: The Harvey-trained group was slightly but not significantly better than the CD-trained group at identifying heart sounds (3.11 versus 2.47, respectively; P = 0.06). However, there was no difference between the Harvey and CD-trained groups in diagnosis (2.94 versus 2.84, respectively), communication skills (18.9 versus 19.6, respectively) or examination skills (17.4 versus 17.5, respectively). CONCLUSIONS: The study found little evidence that students trained with a high-fidelity simulator were more able to transfer skills to real patients than a control group. Although there was some suggestion that the Harvey-trained group was better at recognising heart sounds, there was no difference between groups in diagnostic accuracy or clinical skills.
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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 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".