Consensus Core Point‐of‐care Ultrasound Applications for Pediatric Emergency Medicine Training
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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