EFSUMB Clinical Practice Guidelines for Point-of-Care Ultrasound: Part One (Common Heart and Pulmonary Applications) LONG VERSION
Bibliographic record
Abstract
AIMS: To evaluate the evidence and produce a summary and recommendations for the most common heart and lung applications of point-of-care ultrasound (PoCUS). METHODS: We reviewed 10 clinical domains/questions related to common heart and lung applications of PoCUS. Following review of the evidence, a summary and recommendation were produced, including assignment of levels of evidence (LoE) and grading of the recommendation, assessment, development, and evaluation (GRADE). 38 international experts, the expert review group (ERG), were invited to review the evidence presented for each question. A level of agreement of over 75 % was required to progress to the next section. The ERG then reviewed and indicated their level of agreement regarding the summary and recommendation for each question (using a 5-point Likert scale), which was approved if a level of agreement of greater than 75 % was reached. A level of agreement was defined as a summary of "strongly agree" and "agree" on the Likert scale responses. FINDINGS AND RECOMMENDATIONS: One question achieved a strong consensus for an assigned LoE of 3 and a weak GRADE recommendation (question 1). The remaining 9 questions achieved broad agreement with one assigned an LoE of 4 and weak GRADE recommendation (question 2), three achieving an LoE of 3 with a weak GRADE recommendation (questions 3-5), three achieved an LoE of 3 with a strong GRADE recommendation (questions 6-8), and the remaining two were assigned an LoE of 2 with a strong GRADE recommendation (questions 9 and 10). CONCLUSION: These consensus-derived recommendations should aid clinical practice and highlight areas of further research for PoCUS in acute settings.
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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 | 0.039 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.026 | 0.024 |
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".