Digital Simulation–Based Ultrasound Training in Physiotherapy Students: Blinded Randomized Controlled Trial Applying Item Response Theory (Preprint)
Bibliographic record
Abstract
Background: Ultrasound education has traditionally relied on on-site training, but scalable digital solutions are increasingly needed. Objective: This study aimed to evaluate the effectiveness of a simulation-based online ultrasound platform compared with traditional on-site training. Methods: A prospective randomized blinded study was conducted with 68 physiotherapy students (n=34 per group). Participants were assigned to a simulation-based online training platform (WAZO) or traditional on-site instruction. Both groups completed identical theoretical and practical assessments. Item response theory using a Rasch model was applied to evaluate item difficulty and student ability. Results: No significant differences were found between the online and on-site groups in theoretical scores (mean 4.94, SD 1.47 vs mean 5.08, SD 1.14; P=.65) or practical performance variables, including probe handling (26/34, 76.5% vs 28/34, 82.4%; P=.37) and structure identification (24/34, 70.6% vs 22/34, 64.7%; P=.19). Measurement outcomes also showed no significant differences, including structure diameter (mean 3.78, SD 0.79 mm vs mean 3.98, SD 1.27 mm; P=.46) and structure surface distance (mean 3.94, SD 1.97 mm vs mean 3.24, SD 0.64 mm; P=.06). Item response theory analysis identified image optimization, structure diameter, and structure surface distance as the most difficult items, while probe handling and structure identification were the most informative. The model demonstrated high discriminative performance (area under the curve=0.93), with sensitivity of 87% and specificity of 80%. Conclusions: Simulation-based online ultrasound training provides comparable theoretical and practical outcomes to traditional on-site instruction, supporting its use as a scalable and accessible educational alternative.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".