The Ultrasound Competency Assessment Tool (UCAT): Development and Evaluation of a Novel Competency‐based Assessment Tool for Point‐of‐care Ultrasound
Bibliographic record
Abstract
Abstract Objectives Point‐of‐care ultrasound (POCUS) has become an integral diagnostic and interventional tool. Barriers to POCUS training persist, and it continues to remain heterogeneous across training programs. Structured POCUS assessment tools exist, but remain limited in their feasibility, acceptability, reliability, and validity; none of these tools are entrustment‐based. The objective of this study was to derive a simple, entrustment‐based POCUS competency assessment tool and pilot it in an assessment setting. Methods This study was composed of two phases. First, a three‐step modified Delphi design surveyed 60 members of the Canadian Association of Emergency Physicians Emergency Ultrasound Committee (EUC) to derive the anchors for the tool. Subsequently, the derived ultrasound competency assessment tool (UCAT) was used to assess trainee (N = 37) performance on a simulated FAST examination. The intraclass correlation (ICC) for inter‐rater reliability and Cronbach's alpha for internal consistency were calculated. A statistical analysis was performed to compare the UCAT to other competency surrogates. Results The three‐round Delphi had 22, 26, and 26 responses from the EUC members. Consensus was reached, and anchors for the domains of preparation, image acquisition, image optimization, and clinical integration achieved approval rates between 92 and 96%. The UCAT pilot revealed excellent inter‐rater reliability (with ICC values of 0.69‐0.89; p < 0.01) and high internal consistency (α = 0.91). While UCAT scores were not impacted by level of training, they were significantly impacted by the number of previous POCUS studies completed. Conclusions We developed and successfully piloted the UCAT, an entrustment‐based bedside POCUS competency assessment tool suitable for rapid deployment. The findings from this study indicate early validity evidence for the use of the UCAT as an assessment of trainee POCUS competence on FAST. The UCAT should be trialed in different populations performing several POCUS study types.
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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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".