Distributed Simulation in surgical training: An off-site feasibility study
Bibliographic record
Abstract
BACKGROUND: Simulation offers recognised training benefits, but the cost of high-fidelity contextualised simulation is prohibitive and its accessibility limited to specialised Distributed Simulation centres. Distributed simulation (DS) is an innovative concept of low-cost, portable and high-fidelity contextualised simulation. However, it has previously only been trialled at a central London teaching hospital. AIMS: (1) To explore the off-site feasibility of DS. (2) To determine the response of end-users to DS. METHODS: A DS naive researcher recreated a standardised porcine laparoscopic cholecystectomy scenario at a District General Hospital using DS. A research diary detailed the logistical feasibility of the project, whilst mixed methods were used to determine the response of the 10 surgeons who completed the full-team simulation. RESULTS: DS is feasible off-site with end-users comparing it favourably to their previous simulation experiences. Surgeons perceived DS as being most useful for building the operative confidence of juniors between learning the basics on a bench top model and before entering the operating theatre. CONCLUSIONS: DS has the potential to provide high-fidelity contextualised simulation as an adjunct to, and not a replacement for, surgical training. Unlike other modalities, it is low cost and portable, thereby addressing concerns over affordability and accessibility.
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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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".