Development and Validation of an Assessment Tool for Competency in Critical Care Ultrasound
Bibliographic record
Abstract
BACKGROUND: Point-of-care ultrasound is an emerging technology in critical care medicine. Despite requirements for critical care medicine fellowship programs to demonstrate knowledge and competency in point-of-care ultrasound, tools to guide competency-based training are lacking. OBJECTIVE: We describe the development and validity arguments of a competency assessment tool for critical care ultrasound. METHODS: A modified Delphi method was used to develop behaviorally anchored checklists for 2 ultrasound applications: "Perform deep venous thrombosis study (DVT)" and "Qualify left ventricular function using parasternal long axis and parasternal short axis views (Echo)." One live rater and 1 video rater evaluated performance of 28 fellows. A second video rater evaluated a subset of 10 fellows. Validity evidence for content, response process, and internal consistency was assessed. RESULTS: An expert panel finalized checklists after 2 rounds of a modified Delphi method. The DVT checklist consisted of 13 items, including 1.00 global rating step (GRS). The Echo checklist consisted of 14 items, and included 1.00 GRS for each of 2 views. Interrater reliability evaluated with a Cohen kappa between the live and video rater was 1.00 for the DVT GRS, 0.44 for the PSLA GRS, and 0.58 for the PSSA GRS. Cronbach α was 0.85 for DVT and 0.92 for Echo. CONCLUSIONS: The findings offer preliminary evidence for the validity of competency assessment tools for 2 applications of critical care ultrasound and data on live versus video raters.
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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 | 0.087 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".