The ultrasound competency assessment tool for four-view cardiac POCUS
Bibliographic record
Abstract
Abstract Background Point-of-care ultrasound (POCUS) has been recognized as an essential skill across medicine. However, a lack of reliable and streamlined POCUS assessment tools with demonstrated validity remains a significant barrier to widespread clinical integration. The ultrasound competency assessment tool (UCAT) was derived to be a simple, entrustment-based competency assessment tool applicable to multiple POCUS applications. When used to assess a FAST, the UCAT demonstrated high internal consistency and moderate-to-excellent inter-rater reliability. The objective of this study was to validate the UCAT for assessment of a four-view transthoracic cardiac POCUS. Results Twenty-two trainees performed a four-view transthoracic cardiac POCUS in a simulated environment while being assessed by two observers. When used to assess a four-view cardiac POCUS the UCAT retained its high internal consistency ( $$\alpha =0.90)$$ α = 0.90 ) and moderate-to-excellent inter-rater reliability (ICCs = 0.61–0.91; p ’s ≤ 0.01) across all domains. The regression analysis suggestion that level of training, previous number of focused cardiac ultrasound, previous number of total scans, self-rated entrustment, and intent to pursue certification statistically significantly predicted UCAT entrustment scores [F (5,16) = 4.06, p = 0.01; R 2 = 0.56]. Conclusion This study confirms the UCAT is a valid assessment tool for four-view transthoracic cardiac POCUS. The findings from this work and previous studies on the UCAT demonstrate the utility and flexibility of the UCAT tool across multiple POCUS applications and present a promising way forward for POCUS competency assessment.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 |
| 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".