A Novel Multidimensional Fidelity Framework for Cardiac Surgery Simulation: A Thematic Literature Review
Bibliographic record
Abstract
Introduction: Simulation-based training is increasingly being adopted in cardiac surgery to train future surgeons. Although low- and high-fidelity cardiac surgical simulations have been described previously, simulation fidelity or the degree to which a simulation replicates reality is poorly defined and not well established. This study examines the literature on the themes of fidelity using a novel multidimensional surgical framework. Methods: A keyword-based literature review was conducted to retrieve published cardiac surgical simulation studies using MEDLINE and EMBASE, from January 2000 to February 2020. The search was limited to the date of publication and not by type. Within predefined dimensions, the included articles were thematically analyzed using a hybrid coding approach to identify fidelity themes and subthemes. Results: Twenty-six articles were included in the thematic analysis after duplicate removal, screening, and eligibility assessment based on the inclusion and exclusion criteria. Seven themes were identified within physical, surgical, and interactional dimensions. They were derived from environmental, equipment, anatomical, physiological, procedural, perceptual, and psychological simulation components. Subthemes for three levels of realism were generated for each theme by using an iterative process. Conclusion: This fidelity framework provides educators with actionable guidance for designing cost-effective cardiac surgical simulation for competency-based training by enabling selective fidelity utilization. Educators can apply this framework through aligning learning objectives, fidelity dimensions and levels accordingly. The framework facilitates optimal resource allocation by designing effective and fit for learner simulations.
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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 | 0.026 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.030 | 0.022 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".