Pediatric Emergency Medicine Ultrasound Fellowship Programs
Bibliographic record
Abstract
Point of care ultrasound (POCUS) has undergone important growth in the field of Pediatric Emergency Medicine (PEM) in the last 14 years and is recognized as a critical diagnostic tool in the care of ill and injured children. The first PEM POCUS fellowship was established in 2010. Now, there are currently 30 ultrasound fellowships that offer training to PEM physicians. In 2014, 46 PEM POCUS leaders established the P2 (PEM POCUS) Network (www.P2network.org). This serves as a platform for sharing expertise, building research collaborations, and offering mentorship in the use of POCUS in PEM. In 2019, a multinational group of experts in PEM POCUS published the first consensus guidelines for prioritizing core applications of POCUS, which are fundamental to PEM fellowship training 1. In 2022, the international research priorities for PEM POCUS were published 2. In the same year, the development of a consensus-based definition of focused assessment with sonography for trauma (FAST) in children was established 3.
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.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.117 | 0.022 |
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".