Evaluation of a point-of-care ultrasound scan list in a resource-limited emergency centre in Addis Ababa Ethiopia
Bibliographic record
Abstract
INTRODUCTION: Emergency centres (EC) in low- and middle-income countries often have limited diagnostic imaging capabilities. Point-of-care ultrasound (POCUS) is used in high-income countries to diagnose and guide treatment of life-threatening conditions. This study aims to identify high impact POCUS scans most relevant to practice in an Ethiopian EC. METHODS: A prospective observational study where patients presenting to Tikur Anbessa Specialized Hospital EC in Addis Ababa were eligible for inclusion. Physicians referred patients with a clinical indication for POCUS from a pre-determined 15-scan list. Scans were performed and interpreted, at the bedside, by qualified emergency physicians with POCUS training. RESULTS: A convenience sample of 118 patients with clinical indications for POCUS was enrolled. The mean age was 35 years and 42% were female. In total, 338 scans were performed for 145 indications in 118 patients. The most common scans performed were pericardial (n = 78; 23%), abdominal free fluid (n = 73; 22%), pleural effusion/haemothorax (n = 51; 15%), inferior vena cava (n = 43; 13%), pneumothorax (n = 38; 11%), and global cardiac activity (n = 25; 7%). One hundred and twelve (95%) POCUS scans provided clinically useful information. In 53 (45%) patients, ultrasound findings changed patient management plans by altering the working diagnosis (n = 32; 27%), resulting in a new treatment intervention (n = 28; 24%), resulting in a procedure/surgical intervention (n = 17; 14%) leading to consultation with a specialist (n = 16; 14%), and/or changing a disposition decision (n = 9; 8%). DISCUSSION: In this urban, low-resource, academic EC in Ethiopia, POCUS provided clinically relevant information for patient management, particularly for polytrauma, undifferentiated shock and undifferentiated dyspnea. Results have subsequently been used to develop a locally relevant emergency department ultrasound curriculum for Ethiopia's first emergency medicine residency program.
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How this classification was reachedexpand
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".