Sonographic Accuracy as a Novel Tool for Point‐of‐care Ultrasound Competency Assessment
Bibliographic record
Abstract
OBJECTIVES: The Focused Assessment with Sonography in Trauma (FAST) is a point-of-care ultrasound (PoCUS) study that is routine in trauma patient assessment. Many organizations have published training guidelines, which grant competency through the completion of a fixed number of observed scans. This approach is incongruent with current trends in competency-based medical education. We aim to objectively quantify probe motion and user accuracy to differentiate groups of PoCUS operators. METHODS: 9) after completing a curriculum and repeated the assessment using the identical experimental construct. RESULTS: Significant differences (p < 0.05) were found between the novice and both the intermediate and the novice returned groups in time, path length, and points of interest (POIs) scanned. Novices required more time to complete the full examination (290.82 seconds vs. 197.41 seconds vs. 271.79 seconds), utilized more motion (9392.07 mm vs. 4052.73 mm vs. 4985.05 mm), and imaged fewer POIs (48.13% vs. 95.00% vs. 100.00%) when compared to intermediates and returning novices, respectively. No difference was found between the intermediate and novice returned groups for the complete examination. Spearman's correlation was calculated between variables within each group. Correlations between time and path length were statistically significant (p < 0.05) with novice, intermediate, and novice returned values of 0.67, 0.65, and 0.90. Interestingly, neither time nor path length consistently correlated with POIs scanned in any group. CONCLUSION: Differences in probe motion efficiency and POIs scanned between novices and intermediate or returning novice users show promise for use as a quantitative objective assessment tool. Unlike in surgical literature, accuracy did not correlate with path length or time to examination completion.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".