International Multispecialty Consensus on How to Evaluate Ultrasound Competence: A Delphi Consensus Survey
Bibliographic record
Abstract
OBJECTIVES: To achieve international consensus across multiple specialties on a generic ultrasound rating scale using a Delphi technique. METHODS: Ultrasound experts from Obstetrics-Gynaecology, Surgery, Urology, Radiology, Rheumatology, Emergency Medicine, and Gastro-Enterology representing North America, Australia, and Europe were identified. A multi-round survey was conducted to obtain consensus between these experts. Of 60 invited experts, 44 experts agreed to participate in the first Delphi round, 41 remained in the second round, and 37 completed the third Delphi round. Seven key elements of the ultrasound examination were identified from existing literature and recommendations from international ultrasound societies. All experts rated the importance of these seven elements on a five-point Likert scale in the first round and suggested potential new elements for the assessment of ultrasound skills. In the second round, the experts re-rated all elements and a third round was conducted to allow final comments. Agreement on which elements to include in the final rating scale was pre-defined as more than 80% of the experts rating an element four or five, on importance to the ultrasound examination. RESULTS: Two additional elements were suggested by more than 10% of the experts in the first Delphi round. Consensus was obtained to include these two new elements along with five of the original elements in the final assessment instrument: 1) Indication for the examination 2) Applied knowledge of ultrasound equipment 3) Image optimization 4) Systematic examination 5) Interpretation of images 6) Documentation of examination and 7) Medical decision making. CONCLUSION: International multispecialty consensus was achieved on the content of a generic ultrasound rating scale. This is the first step to ensure valid assessment of clinicians in different medical specialties using ultrasound.
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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 | 0.268 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".