The Rapid Assessment of Competency in Echocardiography Scale
Bibliographic record
Abstract
OBJECTIVES: Increased use of point-of-care ultrasound (US) requires the development of assessment tools that measure the competency of learners. In this study, we developed and tested a tool to assess the quality of point-of-care cardiac US studies performed by novices. METHODS: In phase 1, the Rapid Assessment of Competency in Echocardiography (RACE) scale was developed on the basis of structured interviews with subject matter experts; the tool was then piloted on a small series of US studies in phase 2. In phase 3, the tool was applied to a sample of 154 point-of-care US studies performed by 12 learners; each study was independently rated by 2 experts, with quantitative analysis subsequently performed. RESULTS: Evidence of the content validity of the RACE scale was supported by a consensus exercise, wherein experts agreed on the assessment dimensions and specific items that made up the RACE scale. The tool showed good inter-rater reliability. An analysis of inter-item correlations provided support for the internal structure of the scale, and the tool was able to discriminate between learners early in their point-of-care US learning and those who were more advanced in their training. CONCLUSIONS: The RACE scale provides a straightforward means to assess learner performance with minimal requirements for evaluator training. Our results support the conclusion that the tool is an effective means of making valid judgments regarding competency in point-of-care cardiac US.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".