Evaluation of an International Point of Care Ultrasound (POCUS) Training Program for Internal Medicine Physicians
Bibliographic record
Abstract
Background: Point of care ultrasound (POCUS) training in internal medicine (IM) training remains largely unavailable in lower-resourced health systems globally. Longitudinal inter-institutional collaboration, based in health equity principles, offers a potential mechanism for more accessible and effective IM POCUS education. Methods: In a partnership between two academic medical centers in Caracas, Venezuela (Luis Razetti School of Medicine at the Universidad Central de Venezuela (UCV)) and New York, USA (New York University (NYU) Grossman School of Medicine), we evaluated the impact of an IM POCUS training program on knowledge and skills of IM physicians at UCV. During 2023-2024, 18 UCV IM physicians participated in the program. The program included online tutorials and quizzes, in-person image interpretation review, and supervised practice. Participants completed a pre-course knowledge assessment, post-course knowledge, skills, and self-confidence assessments, and qualitative feedback regarding course acceptability. Results: Pre-to-post knowledge assessments demonstrated mean score improvement. Post-course knowledge scores were not significantly different between UCV and NYU cohorts (77% vs. 78%, respectively; p =0.82). Skill scores measured by a hands-on test were comparable between groups, with few significant differences. Learners self-rated increases in confidence during the course, and rated the course as locally acceptable and sustainable. Conclusions: A standardized, longitudinal, international IM POCUS training program was successfully implemented with faculty learners in a lower-resourced health system, who demonstrated gains in knowledge and skills, and reported high educational value of the partnership. The results support expanding inter-institutional POCUS training programs founded in health equity principles.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".