Investigating the Perceived Efficacy of a Silicone Suturing Task Trainer Using Input from Novice Medical Trainees
Bibliographic record
Abstract
Suturing is an essential procedural skill that medical students are expected to be competent in before they graduate medical school; however, there is often a lack of suturing instruction and practice in undergraduate medicine curriculums. Silicone suturing task trainers created from 3D printed molds can help address this gap in medical education by improving student's manual skills before they perform procedures on real patients upon graduation. Commercially available suture task trainers on the market today lack validation from medical learners; therefore, this study aimed to evaluate the perceived efficacy of a silicone skin suture task trainer created from a 3D printed mold using input from novice medical trainees. A silicone task trainer created by MUN Med 3D was used to teach suturing during two surgery interest group skill development sessions. At the end of the sessions, 38 medical students completed a product evaluation survey that assessed the perceived educational efficacy of the suturing task trainer. Overall, the feedback received from the participants was positive and supported the use of silicone suturing task trainers in undergraduate medicine curriculums.
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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 | 0.005 | 0.017 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".