High-Fidelity Simulation of Transcutaneous Cardiac Pacing
Bibliographic record
Abstract
INTRODUCTION: Transcutaneous cardiac pacing (TCP) is a potentially lifesaving technique that is part of the recommended treatment for symptomatic bradycardia. Transcutaneous cardiac pacing however is used uncommonly, and its successful application is not straightforward. Simulation could, therefore, play an important role in the teaching and assessment of TCP competence. However, even the highest-fidelity mannequins available on the market have important shortcomings, which limit the potential of simulation. METHODS: Six criteria defining clinical competency in TCP were established and used as a starting point in the creation of an improved TCP simulator. The goal was a model that could be used to assess experienced clinicians, an objective that justifies the additional effort required by the increased fidelity. RESULTS: The proposed 2-mannequin model (TMM) combines a highly modified Human Patient Simulator with a SimMan 3G, the latter being used solely to provide the electrocardiography (ECG) tracing. The TMM improves the potential of simulation to assess experienced clinicians (1) by reproducing key features of TCP, like using the same multifunctional pacing electrodes used clinically, allowing dual ECG monitoring, and responding with upper body twitching when stimulated, but equally importantly (2) by reproducing key pitfalls of the technique, like allowing pacing electrode misplacement and reproducing false signs of ventricular capture, commonly, but erroneously, used clinically to establish that effective pacing has been achieved (like body twitching, electrical artifact on the ECG, and electrical capture without ventricular capture). CONCLUSIONS: The proposed TMM uses a novel combination of 2 high-fidelity mannequins to improve TCP simulation until upgraded mannequins become commercially available.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".