Development and Initial Assessment of a Novel Customized Deep Laceration Simulator for Suturing Training
Bibliographic record
Abstract
Suturing of different layers, such as deep lacerations, is a challenging clinical skill for residents. Currently, there is a lack of general suturing instructions and practice in undergraduate medicine curricula which would add to the education required during residency and could be impactful to patient safety. Therefore, in order to adequately prepare trainees for clinical practice, training in suturing needs to be made more robust and executable. One way to facilitate this is to provide easy access to equipment that can offer good educational value while allowing for adequate repetition of suturing deep lacerations outside of clinical settings, similar to how it has been done for superficial lacerations. Simulation-based medical education addresses this by training residents in healthcare skills in a safe and controlled environment. Our technical report aims to describe the development and initial evaluation of a deep laceration simulator designed to train residents in suturing. The simulator was made using additive manufacturing techniques such as three-dimensional printing and silicone. Feedback on the simulator was provided by Centre Hospitalier de l'Université de Montréal clinicians from various specialties and residents. The simulator was assessed mainly as being easy to use, durable, and having anatomically accurate characteristics. The main improvements suggested were to make the skin thinner, divide the epidermis and dermis, add a fascia, and create a looser and friable layer of fat. Overall, the respondents rated the simulator as a good educational tool with a few minor adjustments.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".