Learning Pediatric Point-of-Care Ultrasound
Bibliographic record
Abstract
OBJECTIVES: Using an education and assessment tool, we examined the number of cases necessary to achieve a performance benchmark in image interpretation of pediatric soft tissue, cardiac, lung, and focused assessment with sonography for trauma (FAST) point-of-care ultrasound (POCUS) applications. We also determined interpretation difficulty scores to derive which cases provided the greatest diagnostic challenges. METHODS: Pediatric emergency physicians participated in web-based pediatric POCUS courses sponsored by their institution as a credentialing priority. Participants deliberately practiced cases until they achieved diagnostic interpretation scores of combined 90% accuracy, sensitivity, and specificity. RESULTS: Of the 463 who enrolled, 379 (81.9%) completed cases. The median (interquartile range) number of cases required to achieve the performance benchmark for soft tissue was 94 (68-128); cardiac, 128 (86-201); lung, 87 (25-118); and FAST, 93 (68-133) (P < 0001). Specifically, cases completed to achieve benchmark were higher for cardiac relative to other applications (P < 0.0001 for all comparisons). In soft tissue cases, a foreign body was more difficult to diagnose than cobblestoning and hypoechoic collections (P = 0.036). Poor cardiac function and abnormal ventricles were more difficult to interpret with accuracy than normal (P < 0.0001) or pericardial effusion cases (P = 0.01). The absence of lung sliding was significantly more difficult to interpret than normal lung cases (P = 0.028). The interpretation difficulty of various FAST imaging findings was not significantly different. CONCLUSIONS: There was a significant variation in number of cases required to reach a performance benchmark. We also identified the specific applications and imaging findings that demonstrated the greatest diagnostic challenges. These data may inform future credentialing guidelines and POCUS learning interventions.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".