Determining the Optimal Soft Tissue Preservation Techniques for Surgical Skills Training
Bibliographic record
Abstract
Introduction Competency Based Medical Education, overseen and implemented by the Royal College of Physicians and Surgeons of Canada, requires realistic simulation of specific tasks called Entrustable Professional Activities (EPAs). Mastery of these tasks is required to proceed in the surgical specialty. Due to the growing need for high fidelity training in postgraduate surgical education, there has been a shift from the use of hard‐fixed to soft‐fixed material for improved realism. Currently, there exists no standardized way of assessing the suitability of various fixation techniques for different EPAs. Objective The current study seeks to establish the most appropriate tissue fixation method for each EPA. Additionally, we aim to develop a standardized matrix to rate the effectiveness of soft‐fixation methods for the purposes of surgical training based on a number of factors such as the EPA, storage, longevity, biohazardous risk, cost, biomechanical properties, and realism. Hypothesis We hypothesize that different soft‐fixation methods possess varying suitabilities for each EPA, which could be deduced through the use of a testing matrix. Methods EPAs will be performed on cadaveric tissues embalmed with various soft‐fixation solutions. In order to establish the solutions that will be used, initial tests will be conducted on porcine material. Each pork hock (the joint between the tibia/fibula and the metatarsals) will be embalmed with one of 8 soft‐fixation solutions. Surgical residents will perform an EPA on each embalmed tissue and record their observations. The residents will respond to statements about each of the embalming solutions using a 5‐point Likert scale. The results from this testing will be used to select the embalming solutions to be used when the study is conducted on human donors. Results Testing of porcine material to determine the most useful fixation technique is expected to be completed in February of 2022. Testing on cadaveric tissue is expected to be completed in July of 2022. Conclusion Gaining an understanding of the suitability of various soft‐fixation methods allows for high fidelity surgical skills training and the maximization of the use of each donor.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".