Investigating the Efficacy of Anatomical Silicone Models Developed from a 3D Printed Mold for Perineal Repair Suturing Simulation
Bibliographic record
Abstract
(Autodesk Inc., San Rafael, CA, USA) into a stereolithography (.stl) file and altered to produce a negative mold. Using a spatula, a fine silicone layer was first applied inside the mold, followed by a small piece of flesh-colored mesh netting material within the perineal surface area, fitting the width of the mold. The mesh was pressed into the thin layer of silicone, which was meant to provide anatomical structure to prevent the sutures from tearing through the silicone. The remainder of the silicone mix was then poured into the mold, which required three hours to fully set before being removed from the mold. Twelve silicone models were produced and used during a one-hour workshop at the Rural and Remote Conference by 16 obstetrics and gynecology residents and practicing rural physicians, and four facilitators. At the end of the workshop, the participants were provided with a qualitative survey and asked to rate the perceived realism and educational effectiveness of the silicone perineum model as compared to pre-existing simulation models that they have used previously. The overall workshop participant feedback was positive, noting that the models provided more realistic visualization for the suturing simulation of first- and second-degree perineal injuries. The silicone models were considered to be useful in simulation training when attempting first- and second-degreeperineum suturing techniques within a confined space. The overall feedback was positive, noting that they provided more realistic visualization experience compared to pre-existing simulation models, such as beef tongues and synthetic sponges. The feedback from the participants and facilitators included thoughts about how to add additional mesh to the silicone model so the subcutaneous and vaginal plane sutures would hold, as well as increase the size of the vaginal canal size to more accurately represent a postpartum repair. There were also suggestions to alter the colour of the model to be flesh-toned as opposed to pink, to more accurately simulate human tissue. Silicone perineum models, created from a 3D printed mold, are an economical training tool as compared to commercially available, cost prohibitive models. They also provide anatomically accurate simulation training opportunities for residents to learn and maintain clinical skills in perineal repair, as compared to beef tongues and synthetic sponges, which have previously been used in obstetrics and gynecology simulation-based medical education.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".