Defining minimum image quality criteria for common diagnostic point‐of‐care ultrasound images: A position statement of the Society of Hospital Medicine
Bibliographic record
Abstract
BACKGROUND: Point-of-care ultrasound (POCUS) use continues to expand across multiple clinical subspecialties, and the need for standardization of training and quality assurance has become increasingly important. Despite the need for training, there are currently no widely accepted multispecialty criteria to define an acceptable quality POCUS image for common POCUS applications used by clinicians. Without such criteria, discrepancies in rating POCUS image quality occur, leading to inconsistencies in training and quality assurance, which can ultimately compromise patient care and safety. METHODS: To address this gap, the Society of Hospital Medicine (SHM) Point-of-care Ultrasound Task Force convened an expert panel of 32 national POCUS experts trained in hospital medicine (n = 24), critical care (n = 4), emergency medicine (n = 3), radiology (n = 2), and cardiology (n = 1) and employed a modified-Delphi approach to develop minimum image quality criteria for five common POCUS applications: heart, lungs, abdomen, lower extremity veins, and skin/soft tissues. RESULTS: After three rounds of voting and group discussion, the panel achieved consensus on a comprehensive list of 215 items to define standard image quality criteria in five different body systems. CONCLUSIONS: These POCUS image quality criteria offer a structured, consensus-based framework for evaluating POCUS images and establish a minimum standard for defining an acceptable quality image. Use of these criteria can improve inter-rater reliability and advance standardization of POCUS imaging, which affects training, quality assurance, and credentialing/privileging practices.
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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 | 0.211 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".