Teaching Ultrasonographic Basic Examination Skills (TUBES): Assessment of an Ultrasound Skill Simulator in Teaching Ultrasound-Guided Paracentesis, a Prospective Observational Study
Bibliographic record
Abstract
Ultrasound guidance during centesis is recommended to reduce the risk of serious complications, improve success rates, and choose appropriate sampling locations and equipment. The aim of this study was to develop an accessible and reusable ultrasound skill simulator for ultrasound guided centesis (USGC). Fifty second-year veterinary students reviewed an instructional video prior to performing two USGC skill tests on the simulator, separated by a period of practice. Following practice there was a significant improvement in scores ( p < .0001), and average score and time improved by 8.6% and 163.3 seconds, respectively. The proportion of successful aspirates improved by 9%, from 66% to 75%. Most post-session survey feedback was positive, and none was negative. Thirty-eight of 49 participants (78%) indicated they would prefer learning this skill with the simulator or in combination with individual practice and/or observation. Ninety-eight percent (49/50) strongly agreed that “this training simulator should be incorporated into the veterinary curriculum,” and 94% (47/50) strongly agreed with the statement, “I would use this training simulator again to practice basic ultrasound skills.” Eighty-four percent (42/50) strongly agreed their skills and knowledge improved using the simulator, and 82% (41/50) strongly agreed they gained confidence with ultrasound using the simulator. The remainder of the responses for these four questions was agreement, and no student disagreed with any of the statements. The authors conclude this is an effective and well-received simulator for teaching ex vivo USGC and recommend incorporation into the veterinary curriculum for basic ultrasound skill training.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".