Validation of the quality of ultrasound imaging and competence (QUICk) score as an objective assessment tool for the FAST examination
Bibliographic record
Abstract
BACKGROUND: The Focused Assessment with Sonography for Trauma (FAST) examination has become a valuable tool in trauma resuscitation. Despite the widespread use of FAST training among traumatologists, no evidence-based guidelines exist to support optimal training requirements or to provide quantitative objective assessments of imaging capabilities. Both Task-Specific Checklist (TSC) and Global Rating Scale (GRS) have been validated as objective skill assessment tools; we developed both types of scoring checklist and assessed them for construct validity with the FAST examination. METHODS: Two scoring checklists, collectively termed the Quality of Ultrasound Imaging and Competence (QUICk) Score, were developed and subjected to a modified Delphi consensus process. Two cohorts of 12 novice and 12 expert sonographers performed the FAST examination and were evaluated by two experts according to the QUICk model. Total scores as well as anatomic subsets were compared via comparison of means, and logistic regression modeling was used to determine sensitivity and specificity. RESULTS: Experts achieved significantly higher total scores than novices on both scoring systems (17.2 vs. 11.1 of 24, p < 0.01 TSC, 29.8 vs. 18.4 of 40, p < 0.01 GRS). Sensitivity (85.7% TSC, 92.9% GRS) and specificity (75.0% TSC, 91.7% GRS) as well as area under the receiver operating characteristic curve (89.9% TSC, 97.6% GRS) were consistent with a highly discriminant tool. CONCLUSION: The QUICk Score is the first validated objective tool for assessment of the quality of FAST examination imaging. Use of this tool may be instrumental in developing an evidence-based minimum-performance standard and for assessing quality-improvement modifications in FAST examination 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 | 0.038 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".