Impact of Point-of-Care Ultrasound in Medical Decision Making: Informing the Development of an Internal Medicine Global Health POCUS Curriculum
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Point-of-care Ultrasound (POCUS) is particularly useful in low-middle income countries (LMICs) where advanced imaging modalities and diagnostics are often unavailable. However, its use among Internal Medicine (IM) practitioners is limited and without standard curricula. This study describes POCUS scans performed by U.S. IM residents rotating in LMICs to provide recommendations for curriculum development. METHODS: IM residents within a global health track performed clinically-indicated POCUS scans at two sites. They logged their interpretations and whether or not the scan changed diagnosis or management. Scans were quality-assured by POCUS experts in the US to validate results. Using the criteria of prevalence, ease of learning, and impact, a framework was developed for a POCUS curriculum for IM practitioners within LMICs. RESULTS: A total of 256 studies were included in analysis. 237 (92.5%) answered the clinical question, 107 (41.8%) changed the diagnosis, and 106 (41.4%) changed management. The most frequently used applications were the Focused Assessment for Sonography for HIV associated TB (FASH) exam, finding fluid (pericardial effusion, pleural effusion, ascites), qualitative assessment of left ventricular function, and assessment for A-lines/B-lines/consolidation. The following scans met ease of learning criteria: FASH-basic, assessment of LV function, A-lines vs. B-lines, and finding fluid. Finding fluid and assessment of LV function changed diagnosis and management most frequently, greater than 50% of the time for each category. DISCUSSION/CONCLUSION: We recommend the following applications as highest yield for inclusion in a POCUS curriculum for IM practitioners within LMICs: finding fluid (pericardial effusion, pleural effusion, ascites) and assessment of gross LV function.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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