Simulation-Based Training in Operating Room: A Review Study
Bibliographic record
Abstract
Introduction: Simulation is an educational technology that facilitates learning and improves learner’s performance. The aim of this study was to introduce simulation-based clinical training in operating room. Methods: In this review article, the keywords “simulation, training, clinical education, operating room training, and simulation in operating room” were used to find Persian and English articles published from 2000-2018 andin the databases of Science Direct, Google scholar, PubMed, SID, and Magiran. Articles related to introduction and application of simulation-based training in operating room were selected and reviewed. Results: Forty-Two articles had addressed the history and importance of using simulation in clinical education, their development methods, types of simulators used in the operating room and importance and types of models designed to evaluate the simulation methods. Examples of these simulations included low-fidelity physical simulators, web-based educational tools, computer-based video training, virtual learning environment systems, learning management systems, laparoscopic surgery such as “McGill Inanimate System” for training and evaluation of laparoscopic skills, simulation-based surgical methods, and realistic computer-controlled mannequins such as “Sim Man 3G”. Conclusion: A wide variety of simulators and models can be used for designing, implementation and evaluation of operating room training. Many of the existing challenges can be overcome with proper planning and educational institutions can develop and expand simulation-based trainings in operating room by understanding the educational potential of this method
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| 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 | 0.000 |
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".