The Assessment of Competency in Thoracic Sonography (ACTS) scale: validation of a tool for point-of-care ultrasound
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it