Teaching Accuracy Through Repeated Gamified Echography Training (TARGET): Assessment of an Ultrasound Skill Simulator in Teaching Ultrasound-Guided Needle Placement, a Prospective Observational Study
Bibliographic record
Abstract
Abstract The increasing use of ultrasound in veterinary private practice and demand for skilled operators upon graduation has placed an increased burden on the ever-dwindling number of academic radiologists. Simulation-based medical education can help prepare for and consequently reduce this burden, allowing for the acquisition of clinical skills through deliberate practice in a safe, controlled, and low-stakes environment. Ultrasound-guided fine needle placement is the foundation for more advanced interventions such as ultrasound-guided fine needle aspirates and centeses. A reusable novel ultrasound skill simulator consisting of metal targets wired to a circuit and suspended in ballistics gel was created to teach ultrasound-guided fine needle placement. Forty-seven second-year veterinary students watched an instructional video and performed two ultrasound-guided fine needle placement skill tests on the simulator with a period of practice between. Significant improvement in time to task completion ( p = .0021) was noted after the period of practice. The majority of student feedback was positive with 89% (42/47) indicating they would use the simulator again to practice and that it should be incorporated into the curriculum, 74% (35/47) indicating their basic skills, knowledge, and confidence using ultrasound improved using the simulator, and 55% (26/47) indicating they could now teach this skill to a peer. The authors suggest further development of this model for ease of manufacture and increased variation in difficulty, and veterinary curriculum incorporation for basic ultrasound-guided fine needle placement 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 | 0.018 |
| 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".