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Record W4396718617 · doi:10.1097/pec.0000000000003161

The Current State of Advanced Pediatric Emergency Medicine Point-of-Care Ultrasound (POCUS) Training

2024· article· en· W4396718617 on OpenAlexaboutno aff
Matthew M. Moake, Nicole Klekowski, Matthew P. Kusulas, Sigmund Kharasch, David Teng, Erika Constantine

Bibliographic record

VenuePediatric Emergency Care · 2024
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePoint of care ultrasoundPediatric emergency medicineIntensive care medicineIntensive careCurrent (fluid)UltrasoundTraining (meteorology)Point of careEmergency medicineMedical emergencyEmergency departmentEmergency physicianRadiologyNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: This study aims to assess the current state of advanced pediatric emergency medicine (PEM) point-of-care ultrasound (POCUS) training in North America, including trends in dedicated PEM POCUS fellowships and alternative advanced POCUS training pathways, to better guide future educational efforts within the field. METHODS: We identified and surveyed 22 PEM POCUS fellowship directors across the United States and Canada regarding PEM POCUS fellowship application trends, potential barriers to pursuing additional POCUS training, and novel training models that meet the needs of the PEM POCUS workforce. RESULTS: The past 5 years have seen a growth in both PEM POCUS fellowship program number and trainee positions available, with a general impression by fellowship directors of a high demand for faculty who have these training credentials. However, there was a discordant drop in fellowship applicants and corresponding match rate in 2022, the cause of which is not clear. A number of programs are offering alternative advanced training options including combined PEM/POCUS fellowships and POCUS tracks within PEM fellowship. CONCLUSION: As POCUS use within PEM evolves, a growing number of advanced training options are being developed. Understanding the motivations and barriers for pursuing advanced POCUS training can help to shape these options going forward, to ensure the experience incorporated within each model meets the needs of trainees, the needs of PEM divisions, and the future needs of our field.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.033
GPT teacher head0.369
Teacher spread0.335 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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