A Low-Cost Sonographic Canine Simulator for Pericardiocentesis Training
Bibliographic record
Abstract
Pericardiocentesis is traditionally taught in clinical settings, which can be stressful. This study aimed to create a low-cost sonographic simulator to improve novice veterinarians' confidence in performing pericardiocentesis. We hypothesized that experienced clinicians would find the simulator realistic, and that novice confidence would improve following simulator practice. Human ethics approval was obtained. The simulator was constructed using ballistic gel, silicon foam, and a 3D-printed spine with ribs. A tennis ball within a balloon, both filled with water, simulated pericardial effusion (PE). Experts, defined as having performed more than five pericardiocentesis procedures and having more than 3 years of emergency experience, assessed the simulator's realism using a five-point Likert scale. Novice confidence levels were surveyed before and after using the simulator. The data were analyzed for normality. Confidence levels were compared using paired t-tests or Wilcoxon tests. Experts rated the sonographic appearance of PE as very realistic (strongly agree to agree) but found the tactile feel of catheter advancement to be somewhat limited (agree to neither agree nor disagree). The comparability to performing pericardiocentesis on a live canine was rated as realistic (agree). Interns showed a significant increase in confidence in their ability to perform pericardiocentesis on live patients ( p = .03), sonographic visualization and guidance of the catheter ( p = .02), and correct catheter placement ( p = .02). The simulator realistically simulates natural canine PE and improves novice confidence in performing pericardiocentesis. Further research is needed to determine if these skills translate to clinical settings.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".