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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".