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 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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".