Validation of the Quality Of Ultrasound Imaging and Competence (QUICk) Score as an Objective Assessment Tool for Use With an Ultrasound Simulator
Bibliographic record
Abstract
Introduction: Simulation has an ever-increasing role in medical education. With the ubiquity of ultrasound and the availability of simulation, robust tools are required to assess learners. The Quality of Ultrasound Imaging and Competence (QUICk) has previously been validated for use on healthy volunteers for the assessment of the Focused Assessment of Sonography in Trauma (FAST) exams. We sought to assess its validity for use with a commercial ultrasound simulator. Methods: Three groups with differing expertise were recruited to participate: novices with no ultrasound training, intermediates who had completed a formal course within six months, and experts with at least five years of clinical experience. All participants were recorded while completing a FAST exam. The video was then scored using the (QUICk) by two expert assessors. Differences among groups were compared using Kruskall-Wallis. Inter-rater agreement was calculated using weighted kappa. Results: Thirty-five participants were recruited with 13 novices, 10 intermediates and 12 experts. Novices had significantly lower scores than both the intermediates and experts on the QUICk checklist total, global rating scale overall and global rating scale total. There was no difference between the intermediates and experts. Weight kappa showed excellent agreement for both the checklist total and global rating scale total. Conclusion: The QUICk score is a valid tool for assessing FAST exam using a commercial ultrasound simulator. This tool can be incorporated into educational curricula to set minimum-performance standards and quality improvement for FAST 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 | 0.015 | 0.034 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".