The knowledge and skills needed to perform intestinal ultrasound for inflammatory bowel diseases—an international Delphi consensus survey
Bibliographic record
Abstract
BACKGROUND: Intestinal ultrasound (IUS) is a non-invasive modality for monitoring disease activity in inflammatory bowel diseases (IBD). IUS training currently lacks well-defined standards and international consensus on competency criteria. AIM: To achieve international consensus on what competencies should be expected from a newly certified IUS practitioner. METHODS: A three-round, iterative Delphi process was conducted among 54 IUS experts from 17 countries. Round 1 was a brainstorming phase with an open-ended question to identify the knowledge and skills that experts believe a newly certified IUS practitioner should possess. The experts' suggestions were then organised into statements by a Steering Committee. In round 2, the experts commented upon and rated the statements, which were revised accordingly. In round 3, the experts rated the revised statements. Statements meeting the pre-defined consensus criterion of at least 70% agreement were included in the final list of statements. RESULTS: In total, 858 items were suggested by the experts in the first round. Based on these suggestions, 55 statements were organised into three categories: knowledge, technical skills and interpretation skills. After the second round, 53 revised statements remained. After the final round, a total of 41 statements had achieved consensus. CONCLUSIONS: We established international, expert consensus on the knowledge and skills that should be expected from newly certified IUS practitioners. These consensus statements are the first step towards mastery learning for IUS training. Educators can utilise these statements to design training programmes and evaluate the competencies of trainees before they engage in independent 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 | 0.038 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".