Point-of-Care Lung Ultrasound in Emergency Medicine
Bibliographic record
Abstract
BACKGROUND: This scoping review was conducted to provide an overview of the evidence of point-of-care lung ultrasound (LUS) in emergency medicine. By emphasizing clinical topics, time trends, study designs, and the scope of the primary outcomes, a map is provided for physicians and researchers to guide their future initiatives. RESEARCH QUESTION: Which study designs and primary outcomes are reported in published studies of LUS in emergency medicine? STUDY DESIGN AND METHODS: We performed a systematic search in the PubMed/MEDLINE, Embase, Web of Science, Scopus, and Cochrane Library databases for LUS studies published prior to May 13, 2023. Study characteristics were synthesized quantitatively. The primary outcomes in all papers were categorized into the hierarchical Fryback and Thornbury levels. RESULTS: A total of 4,076 papers were screened and, following selection and handsearching, 406 papers were included. The number of publications doubled from January 2020 to May 2023 (204 to 406 papers). The study designs were primarily observational (n = 375 [92%]), followed by randomized (n = 18 [4%]) and case series (n = 13 [3%]). The primary outcome measure concerned diagnostic accuracy in 319 papers (79%), diagnostic thinking in 32 (8%), therapeutic changes in 4 (1%), and patient outcomes in 14 (3%). No increase in the proportions of randomized controlled trials or the scope of primary outcome measures was observed with time. A freely available interactive database was created to enable readers to search for any given interest (https://public.tableau.com/app/profile/blinded/viz/LUSinEM_240216/INFO). INTERPRETATION: Observational diagnostic studies have been produced in abundance, leaving a paucity of research exploring clinical utility. Notably, research exploring whether LUS causes changes to clinical decisions is imperative prior to any further research being made into patient benefits.
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.
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 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".