PD08-07 DEVELOPMENT AND INITIAL VALIDATION OF A LOW-COST ULTRASOUND-COMPATIBLE SUPRAPUBIC CATHETER INSERTION TRAINING SIMULATOR
Bibliographic record
Abstract
You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology II (PD08)1 Apr 2019PD08-07 DEVELOPMENT AND INITIAL VALIDATION OF A LOW-COST ULTRASOUND-COMPATIBLE SUPRAPUBIC CATHETER INSERTION TRAINING SIMULATOR Yuding Wang*, Jen Hoogenes, Udi Blankstein, Ali Al-Hashimi, Bobby Shayegan, and Edward Matsumoto Yuding Wang*Yuding Wang* More articles by this author , Jen HoogenesJen Hoogenes More articles by this author , Udi BlanksteinUdi Blankstein More articles by this author , Ali Al-HashimiAli Al-Hashimi More articles by this author , Bobby ShayeganBobby Shayegan More articles by this author , and Edward MatsumotoEdward Matsumoto More articles by this author View All Author Informationhttps://doi.org/10.1097/01.JU.0000555252.26605.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVES: Bedside suprapubic catheterization (SPC) is a fundamental skill required of all urology trainees. Ultrasound guidance during SPC insertion can minimize complications, and its use is recommended in clinical practice guidelines. However, a lack of affordable simulation models and the unpredictability of bedside SPCs make learning this procedure difficult for trainees. We developed and initially validated a low-cost ultrasound-compatible SPC simulator that allows trainees to safely and deliberately practice the task while acquiring skills that are transferable to bedside SPCs. METHODS: The SPC simulator consists of 7 components (Table 1). 7 staff urologists and 4 interventional radiologists conducted a SPC using the model with ultrasound guidance (Figures 1 and 2). To assess for face and content validity, each participant rated the model using a 5-point Likert scale on 3 domains: anatomic realism, usefulness as a training tool, and overall reaction. RESULTS: Participants were in practice for an average of 10 years (range 2-23), and the median number of SPCs performed was 50. For the domains, anatomic realism scored a mean of 4.1 (mean of 4.0 for sonographic realism). Usefulness as a training tool scored a mean of 4.3, and the mean for overall reaction was 4.4. Participants strongly agreed that the model should be incorporated into urology residency (mean=4.4), the skills are transferable to patients (mean=4.3), and its use would improve trainee confidence (mean=4.6). The cost of the model is approximately $48 CAD, and can be used multiple times during one session. CONCLUSIONS: This novel, low-cost, easily reproducible ultrasound-compatible SPC training simulator received positive evaluations from urologists and interventional radiologists as a useful model for teaching bedside ultrasound-guided SPC insertion. This model will be integrated into our annual urology boot camp curriculum for first-year residents, which will allow for the evaluation of trainees as they learn SPC on the model with instruction and feedback. Additional research is required for construct validation. Source of Funding: None Hamilton, Canada© 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019Page: e150-e151 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Yuding Wang* More articles by this author Jen Hoogenes More articles by this author Udi Blankstein More articles by this author Ali Al-Hashimi More articles by this author Bobby Shayegan More articles by this author Edward Matsumoto More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".