Male catheter insertion simulation using a low-fidelity 3D-printed model in undergraduate medical learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Urinary catheter insertion is one of the most widely performed procedures in a clinical setting. Inexperienced cath- eterizations constitute a high percentage of urethral trauma in hospital settings, with as high as 75% of comorbidities related to inaccurate insertion. Simulation training can help learners feel more confident, shorten the learning curve, and provide a safe learning environment for novices to make, and learn from, mistakes. Three dimensional (3D)- printed simulation models are as effective as commercially available models for novice learners, and have the benefits of being inexpensive, anatomically correct, portable and can be easily modified and rapidly produced as needed. A 3D-printed male urinary catheter insertion simulation model, designed by MUNMed 3D, was offered to Memorial University medical students as part of pre-clerkship procedural training. Fourteen students were provided with a checklist for the procedure and the 3D-printed urinary catheter insertion simulator, and following the simulation, were asked to complete a 5-point Likert survey on their experience.The average self-reported skill before using the model was 1.29 (out of 5), which increased to 3.21 (out of 5). All 14 respondents selected either “agree” or “strongly agree” for the following four survey items: the simulation was an accurate anatomical representation, they would prefer learning on this simulation model before performing this procedure, they would recommend the model to other learners, and they found this model beneficial overall. Simulation training with a 3D-printed urinary catheter insertion simulator allows trainees the opportunity to become confident and familiarize themselves with the procedure before performing it on a real patient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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 | 0.002 |
| 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 it