Consensus Core Point‐of‐care Ultrasound Applications for Pediatric Emergency Medicine Training
Bibliographic record
Abstract
Abstract Background Pediatric emergency medicine (PEM) physicians have variably incorporated point‐of‐care ultrasound (POCUS) into their practice. Prior guidelines describe the scope of POCUS practice for PEM physicians; however, consensus does not yet exist about which applications should be prioritized and taught as fundamental skills for PEM trainees. Members of the PEM POCUS Network (P2Network) conducted a consensus‐building process to determine which applications to incorporate into PEM fellowship training. Methods A multinational group of experts in PEM POCUS was recruited from the P2Network and greater PEM POCUS community if they met the following criteria: performed over 1,000 POCUS scans and had at least 3 years of experience teaching POCUS to PEM fellows, were a local academic POCUS leader, or completed a formal PEM POCUS fellowship. Experts rated 60 possible PEM POCUS applications for their importance to include as part of a PEM fellowship curriculum using a modified Delphi consensus‐building technique. Results In round 1, 66 of 92 (72%) participants responded to an e‐mail survey of which 48 met expert criteria and completed the survey. Consensus was reached to include 18 items in a PEM fellowship curriculum and to exclude two items. The 40 remaining items and seven additional items were considered in round 2. Thirty‐seven of 48 (77%) experts completed round 2 reaching consensus to include three more items and exclude five. The remaining 39 items did not reach consensus for inclusion or exclusion. Conclusion Experts reached consensus on 21 core POCUS applications to include in PEM fellowship curricula.
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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 | 0.143 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".