The Assessment of Competency in Thoracic Sonography (ACTS) scale: validation of a tool for point-of-care ultrasound
Bibliographic record
Abstract
BACKGROUND: The rapid adoption of point-of-care ultrasound (POCUS) has created a need to develop assessment tools to ensure that learners can competently use these technologies. In this study, the authors developed and tested a rating scale to assess the quality of point-of-care thoracic ultrasound studies performed by novices. In Phase 1, the Assessment of Competency in Thoracic Sonography (ACTS) scale was developed based on structured interviews with subject matter experts. The tool was then piloted on a small series of ultrasound studies in Phase 2. In Phase 3 the tool was applied to a sample of 150 POCUS studies performed by ten learners; performance was then assessed by two independent raters. RESULTS: Evidence for the content validity of the ACTS scale was provided by a consensus exercise wherein experts agreed on the general principles and specific items that make up the scale. The tool demonstrated reasonable inter-rater reliability despite minimal requirements for evaluator training and displayed evidence of good internal structure, with related scale items correlating well with each other. Analysis of the aggregate learning curves suggested a rapid early improvement in learner performance with slower improvement after approximately 25-30 studies. CONCLUSIONS: The ACTS scale provides a straightforward means to assess learner performance. Our results support the conclusion that the tool is an effective means of making valid judgments regarding competency in point-of-care thoracic ultrasound, and that the majority of learner improvement occurs during their first 25-30 practice studies.
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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 | 0.035 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 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".