Development and initial validation of a cost-effective, re-usable, ultrasound-compatible suprapubic catheter insertion training simulator
Bibliographic record
Abstract
INTRODUCTION: Suprapubic catheterization (SPC) is a fundamental skill required of urology trainees. A lack of affordable simulation models and unpredictability of bedside SPCs limit experiential learning opportunities. Our objective was to develop and initially validate a re-usable, low-cost, ultrasound (US)-compatible SPC simulator for acquiring skills that transfer to the bedside. METHODS: The model was constructed using six components. Staff urologists and interventional radiologists (IRs) conducted a SPC and rated the model on three domains with multiple subcategories on a five-point Likert scale: anatomic realism; usefulness as a training tool; and global/overall reaction. Participants in our first-year urology "boot camp" received SPC training, practiced, and were evaluated via an objective structured clinical examination (OSCE). Staff ratings and OSCE scores determined the model's initial face and content validity. RESULTS: Twelve staff physicians participated in the study. The mean scores for urologists and IRs, respectively, were: anatomical realism: 4.10 and 3.70; usefulness as a training tool: 4.23 and 4.24; and overall reaction: 4.40 and 4.44. Staff strongly agreed that the model should be incorporated into the residency curriculum. Over the past four years, 25 boot camp participants scored a mean of 99.7% (±1.8) on the OSCE, with high technical performance and entrustment scores (4.8 and 4.7, respectively). The model cost $55 CAD. CONCLUSIONS: This novel, multiple-use, low-cost, easily reproducible US-compatible SPC simulator demonstrated initial face and content validity via high staff urologist and IR ratings and OSCE scores of first-year urology residents. Additional research is required for construct validation.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".