Recommendations for core critical care ultrasound competencies as a part of specialist training in multidisciplinary intensive care: a framework proposed by the European Society of Intensive Care Medicine (ESICM)
Bibliographic record
Abstract
Critical care ultrasound (CCUS) is an essential component of intensive care practice. Although existing international guidelines have focused on training principles and determining competency in CCUS, few countries have managed to operationalize this guidance into an accessible, well-structured programme for clinicians training in multidisciplinary intensive care. We seek to update and reaffirm appropriate CCUS scope so that it may be integrated into the international Competency-based Training in Intensive Care Medicine. The resulting recommendations offer the most contemporary and evolved set of core CCUS competencies for an intensive care clinician yet described. Importantly, we discuss the rationale for inclusion but also exclusion of competencies listed. BACKGROUND/AIM: Critical care ultrasound (CCUS) is an essential component of intensive care practice. The purpose of this consensus document is to determine those CCUS competencies that should be a mandatory part of training in multidisciplinary intensive care. METHODS: A three-round Delphi method followed by face-to-face meeting among 32 CCUS experts nominated by the European Society of Intensive Care Medicine. Agreement of at least 90% of experts was needed in order to enlist a competency as mandatory. RESULTS: The final list of competencies includes 15 echocardiographic, 5 thoracic, 4 abdominal, deep vein thrombosis diagnosis and central venous access aid. CONCLUSION: The resulting recommendations offer the most contemporary and evolved set of core CCUS competencies for an intensive care clinician yet described.
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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 | 0.064 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".