Developing and Evaluating a Remote Quality Assurance System for Point-of-Care Ultrasound for an Internal Medicine Residency Global Health Track
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
Background: A quality assurance system is vital when using point-of-care ultrasound (POCUS) to ensure safe and effective ultrasound use. There are many barriers to implementing a quality assurance system including need for costly software, faculty time, and extra work to log images. Methods: With minimal funding or protected faculty time, we successfully developed an effective remote quality assurance system between residents rotating internationally and faculty in the US. Results: 270 total exams were logged using this system (41 per resident over a 7 week period). Over the course of the implementation period, a significant increase was seen in average image quality (p = 0.030) and percent agreement with reviewer (p = 0.021). No significant increase was seen for percent images with quality rating 5/5 (p = 0.068) or for studies per resident per week (p = 0.30). Discussion/Conclusions: A quality assurance system for remote review and feedback of POCUS exams was successfully developed with limited available funding, using consumer-level software and an educational collaboration. Residents used the system regularly and demonstrated improvement in reviewer-rated image acquisition and interpretation skills. A similar system can be applied for physicians in any geographic area looking to learn POCUS, in partnership with local or international POCUS mentors. We detail a step-by-step approach, challenges encountered, and lessons learned, to help guide others seeking to implement similar programs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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