Focused Critical Care Echocardiography: Development and Evaluation of an Image Acquisition Assessment Tool*
Bibliographic record
Abstract
OBJECTIVES: Little attention has been placed on assessment tools to evaluate image acquisition quality for focused critical care echocardiography. We designed a novel assessment tool to objectively evaluate the image acquisition skills of critical care trainees learning focused critical care echocardiography and examined the tool for evidence of validity. DESIGN: Prospective observational study. SETTING: Medical-surgical ICUs at a tertiary care teaching hospital. SUBJECTS: Trainees in our critical care medicine fellowship program. INTERVENTIONS: Six trainees completed a focused critical care echocardiography training curriculum followed by performing 20 transthoracic echocardiograms on patients receiving invasive mechanical ventilation. At three assessment intervals (the 1st and 2nd examinations, 10th and 11th examinations, and 19th and 20th examinations), echocardiograms performed by trainees were compared with those of critical care physicians certified in echocardiography and scored according to the focused critical care echocardiography assessment tool. The primary outcome was an efficiency score (overall assessment tool score divided by examination time). Differences in mean efficiency scores between echocardiographers of differing skill levels and changes in trainees' mean efficiency scores with increasing focused critical care echocardiography experience were compared by using t tests. MEASUREMENTS AND MAIN RESULTS: On the initial assessment, mean efficiency scores (SD) for trainees and experienced physicians were 1.55 (0.95) versus 2.78 (1.38), respectively (p = 0.02), and for the second and third assessments, the corresponding efficiency ratings for trainees and experienced physicians were 2.48 (0.97) versus 4.55 (1.32) (p < 0.01) and 2.61 (1.37) versus 4.17 (2.12) (p = 0.04), respectively. CONCLUSIONS: Trainees' efficiency in focused critical care echocardiography image acquisition improved quickly in the first 10 studies, yet, it could not match with the performance of experienced physicians after 20 focused critical care echocardiography studies. The focused critical care echocardiography assessment tool demonstrated evidence of validity and could discern changes in trainees' image acquisition performance with increasing experience.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".