Development of a Focused Cardiac Ultrasound Image Acquisition Assessment Tool
Bibliographic record
Abstract
Abstract Background Focused cardiac ultrasound (FCU) is widely used by healthcare providers to answer specific questions about cardiac structure and function at the bedside. Currently, no widely accepted FCU image acquisition checklist exists to assess learners with varying skill levels from different specialties. Objective The primary objective of this project was to develop a consensus-based FCU image acquisition checklist using a multispecialty group of point-of-care ultrasound (POCUS) experts. Methods The essential components of an FCU examination were identified on the basis of published recommendations from echocardiography and international ultrasound societies. A checklist of the essential components of an FCU examination was drafted. A panel of POCUS experts from different medical specialties in the United States and Canada was convened to vote on each checklist item by answering two questions: 1) Is this item important to include in a checklist of essential FCU skills applicable to any medical specialty? and 2) Should the learner be required to successfully complete this item to be considered competent? A modified Delphi approach was used to assess the level of agreement for each checklist item during four rounds of voting. Checklist items that achieved an agreement of 80% or greater were included in the final checklist. Results Thirty-one POCUS experts from seven different medical specialties voted on sixty-five items to be included in the FCU image acquisition assessment tool. The majority of POCUS experts (61%) completed all four rounds of voting. During the first round of voting, 59 items reached consensus, and after revision and revoting, an additional 3 items achieved 80% or greater consensus. A total of 62 items were included in the final checklist, and 57 items reached consensus as a requirement for demonstration of competency. Conclusion We have developed a multispecialty, consensus-based FCU image acquisition checklist that may be used to assess the skills of learners from different specialties. Future steps include studies to develop additional validity evidence for the use of the FCU assessment tool and to evaluate its utility for the translation of skills into clinical practice.
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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 | 0.045 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".