Validation of a proposed objective assessment tool for ultrasound image acquisition utilizing the focused assessment with sonography for trauma examination
Bibliographic record
Abstract
Introduction: No protocol for assessing ultrasound imaging skill has been validated. We sought to develop and validate an assessment protocol for ultrasound imaging for the Focused Assessment with Sonography for Trauma. Methods: Our assessment tool consisted of task checklists, a global rating scale, and hand-motion analysis and was developed by a modified Delphi technique. Novice and expert cohorts were recruited to perform a FAST exam on a volunteer for assessment under the protocol. Results: Experts scored higher on static image acquisition (11.58 of 16 versus 6.63, p<0.0001), dynamic image acquisition (17.21 of 24 versus 11.08, p=0.0005), and our global rating scale (29.79 of 40 versus 18.42, p<0.0001); experts used fewer movements (263.0 movements versus 452.4, p=0.0216) and a shorter path length than novices (60.097 m versus 32.777 m, p=0.0041). Conclusion: Our protocol for assessing ultrasound imaging skill has criterion validity in assessing expertise and may lead to improvements to training and credentialing programs.
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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 | 0.047 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".