<scp>ULTRA</scp> ‐Metrics
Bibliographic record
Abstract
OBJECTIVES: Ultrasound competency is critical in modern healthcare, yet no standardized framework currently supports ultrasound skill monitoring across diverse clinical settings and user types. Existing frameworks often lack generalizability, overemphasize exam counts, and fail to assess key skills such as interpretation, limiting ultrasound's safe and effective integration into clinical practice. The objective of this study is to develop a consensus-based, universal framework for monitoring ultrasound competency across clinical applications and disciplines. METHODS: A modified Delphi process was conducted with an international panel of Point-of-Care ultrasound experts. Panelists independently evaluated framework elements categorized by competency domains (experience, skills, autonomy), skill domains (indication, acquisition, interpretation, clinical integration), metrics (eg, exam counts, entrustability, interpretation accuracy, etc.), answer sets (score-based inputs used by assessors), and score criteria (requirements for each score). Consensus thresholds were defined as strong consensus at >84%, and weak consensus at 68-84%. Two Delphi rounds were completed. RESULTS: Nineteen experts participated across 2 Delphi rounds. Strong consensus was reached to include 3 competency domains (experience, skills, autonomy) and 4 skill domains (indication, acquisition, interpretation, and clinical integration). Optional components, including the use of acquisition skill trees and varied answer set granularity, were favored by some participants to allow ultrasound programs to tailor the framework to specific examinations, assessment scenarios, and job roles. CONCLUSION: The resulting modular framework provides a flexible, consensus-based approach to ultrasound competency assessment, enabling cross-program comparisons and evaluation of training methods. Validation across diverse settings is needed to support its use in global competency standards and ultrasound education expansion.
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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 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.112 | 0.062 |
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".