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Record W2960321545 · doi:10.1097/pec.0000000000001871

Quality Assurance for Point-of-Care Ultrasound in North American Pediatric Emergency Medicine Fellowships

2019· article· en· W2960321545 on OpenAlexaboutno aff
Rosemary Thomas‐Mohtat, Kristen Breslin, Joanna S. Cohen

Bibliographic record

VenuePediatric Emergency Care · 2019
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePoint of care ultrasoundPediatric emergency medicineCredentialingQuality assuranceMEDLINEFamily medicineEmergency medicineMedical emergencyEmergency departmentMedical educationEmergency physicianNursingPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: The American Academy of Pediatrics, the Society for Academic Emergency Medicine, and the American College of Emergency Physicians released a policy statement endorsing the use of point-of-care ultrasound (POCUS) by pediatric emergency medicine (PEM) providers. This statement specifically recommends that emergency departments have a credentialing and quality assurance (QA) program for POCUS. There is limited knowledge of how QA for POCUS is currently carried out in pediatric emergency departments with PEM training programs. METHODS: We sent a cross-sectional web-based survey to all 81 PEM fellowship-training programs in the United States and Canada between June 2016 and June 2017. RESULTS: Sixty-six of 81 programs (81.2%) responded. Sixty-five percent of responding PEM training programs had POCUS-trained faculty or a POCUS champion at their institution. Forty-six percent had a POCUS fellowship in their institution, with 10 programs having PEM-specific POCUS fellowships. Programs with POCUS fellowships were more likely to save all images, review all scans, review scans more frequently, provide feedback, and bill compared with programs without POCUS fellowships. CONCLUSIONS: Point-of-care ultrasound is growing in PEM fellowship-training programs, with a majority of programs now having faculty members trained or interested specifically in POCUS. Most programs prefer more frequent and thorough QA processes, and programs with POCUS fellowships are more likely to have more frequent and thorough QA processes.

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)0.004
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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.369
Teacher spread0.333 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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