Evaluation of a Novel 3D-Printed Urinary Catheterization Simulation Model in Undergraduate Medical Teaching
Bibliographic record
Abstract
Introduction Urinary catheter insertion is a mandatory procedure taught during medical school. It is imperative that learners are provided the opportunity to practice the procedure, as an improper catheterization technique can result in urethral trauma and contribute to urinary tract infections. Simulation training offers the advantage of avoiding patient harm while allowing learners to feel comfortable to learn from their mistakes, resulting in increased user confidenceand shortening the learning curve for basic procedures. 3D-printed simulation models are anatomically accurate, low-cost, reusable, and effective for teaching basic procedural skills.This study aims to assess the self-rated effectiveness of the 3D model in increasing student confidence and preparedness. Methods Preclerkship undergraduate medical students (n=64) participated in procedural skills training sessions using the 3D-printed model. The students were provided with didactic teaching from a urologist, a hands-on demonstration, and then allowed to practice the procedure using the 3D model. Students were subsequently asked to complete a Likert-type survey to evaluate their experience and the 3D model as an educational tool. Results Respondents felt that the 3D-printed model allowed for the realistic and accurate performance of a urinary catheterization procedure, allowing students to increase their confidence, competence, and knowledge of the technique. Student responses for increasing competence were rated as an average of 4.48±0.62 (where 1 is "not at all effective" and 5 is "very effective"), confidence was rated as 4.40±0.71, and preparedness was reported as 4.15±0.76. Overall, the reported value as a training tool resulted in an average score of 4.62±0.58 (where 1 is "not at all relevant" and 5 is "very relevant"). Conclusions Preclerkship undergraduate medical students found the 3D-printed male catheter insertion model to be a useful learning tool with accurate anatomical representations and technical qualities. The 3D-printed model can be beneficial for increasing learner confidence and preparedness when completing a catheter insertion, allowing for the opportunity to practice on a low-cost, accessible simulator.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".