Best Practices for Point of Care Ultrasound: An Interdisciplinary Expert Consensus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the growing use of point of care ultrasound (POCUS) in contemporary medical practice and the existence of clinical guidelines addressing its specific applications, there remains a lack of standardization and agreement on optimal practices for several areas of POCUS use. The Society of Point of Care Ultrasound (SPOCUS) formed a working group in 2022 to establish a set of recommended best practices for POCUS, applicable to clinicians regardless of their training, specialty, resource setting, or scope of practice. Using a three-round modified Delphi process, a multi-disciplinary panel of 22 POCUS experts based in the United States reached consensus on 57 statements in domains including: (1) The definition and clinical role of POCUS; (2) Training pathways; (3) Credentialing; (4) Cleaning and maintenance of POCUS devices; (5) Consent and education; (6) Security, storage, and sharing of POCUS studies; (7) Uploading, archiving, and reviewing POCUS studies; and (8) Documenting POCUS studies. The consensus statements are provided here. While not intended to establish a standard of care or supersede more targeted guidelines, this document may serve as a useful baseline to guide clinicians, leaders, and systems considering initiation or enhancement of POCUS programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it