Basic ultrasound head-to-toe skills for intensivists in the general and neuro intensive care unit population: consensus and expert recommendations of the European Society of Intensive Care Medicine
Bibliographic record
Abstract
PURPOSE: To provide consensus, and a list of experts' recommendations regarding the basic skills for head-to-toe ultrasonography in the intensive care setting. METHODS: The Executive Committee of the European Society of Intensive Care (ESICM) commissioned the project and supervised the methodology and structure of the consensus. We selected an international panel of 19 expert clinicians-researchers in intensive care unit (ICU) with expertise in critical care ultrasonography (US), plus a non-voting methodologist. The panel was divided into five subgroups (brain, lung, heart, abdomen and vascular ultrasound) which identified the domains and generated a list of questions to be addressed by the panel. A Delphi process based on an iterative approach was used to obtain the final consensus statements. Statements were classified as a strong recommendation (84% of agreement), weak recommendation (74% of agreement), and no recommendation (less than 74%), in favor or against. RESULTS: This consensus produced a total of 74 statements (7 for brain, 20 for lung, 20 for heart, 20 for abdomen, 7 for vascular Ultrasound). We obtained strong agreement in favor for 49 statements (66.2%), 8 weak in favor (10.8%), 3 weak against (4.1%), and no consensus in 14 cases (19.9%). In most cases when consensus was not obtained, it was felt that the skills were considered as too advanced. A research agenda and discussion on training programs were implemented from the results of the consensus. CONCLUSIONS: This consensus provides guidance for the basic use of critical care US and paves the way for the development of training and research projects.
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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 | 0.157 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".