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Record W2547526502 · doi:10.1186/s13089-016-0049-5

Pediatric emergency medicine point-of-care ultrasound: summary of the evidence

2016· review· en· W2547526502 on OpenAlexaff
Jennifer R. Marín, Alyssa Abo, Alexander Arroyo, Stephanie J. Doniger, Jason Fischer, Rachel G. Rempell, Brandi Gary, James F. Holmes, David Kessler, Samuel H. F. Lam, Marla C. Levine, Jason A. Levy, Alice F. Murray, Lorraine Ng, Vicki E. Noble, Daniela Ramirez‐Schrempp, David C. Riley, Turandot Saul, Vaishali Shah, Adam Sivitz, Ee Tein Tay, David Teng, Lindsey Chaudoin, James W. Tsung, Rebecca L. Vieira, Yaffa M. Vitberg, Resa E. Lewiss

Bibliographic record

VenueCritical Ultrasound Journal · 2016
Typereview
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsPoint of care ultrasoundPediatric emergency medicineMedicinePoint of careGuidelineSpecialtyInterventional radiologyCredentialingMEDLINEFamily medicineMedical emergencyEmergency departmentNursingEmergency physicianRadiologyPathology

Abstract

fetched live from OpenAlex

The utility of point-of-care ultrasound is well supported by the medical literature. Consequently, pediatric emergency medicine providers have embraced this technology in everyday practice. Recently, the American Academy of Pediatrics published a policy statement endorsing the use of point-of-care ultrasound by pediatric emergency medicine providers. To date, there is no standard guideline for the practice of point-of-care ultrasound for this specialty. This document serves as an initial step in the detailed "how to" and description of individual point-of-care ultrasound examinations. Pediatric emergency medicine providers should refer to this paper as reference for published research, objectives for learners, and standardized reporting guidelines.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.099
GPT teacher head0.441
Teacher spread0.341 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations186
Published2016
Admission routes1
Has abstractyes

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