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Record W3091490016 · doi:10.1097/mpg.0000000000002964

North American Society for Pediatric Gastroenterology, Hepatology and Nutrition and the Society for Pediatric Radiology Joint Position Paper on Noninvasive Imaging of Pediatric Pancreatitis

2020· review· en· W3091490016 on OpenAlexaff
Andrew T. Trout, Sudha A. Anupindi, A. Jay Freeman, Jorge Alberto Macías-Flores, Jessica Martinez, Kalyan Ray Parashette, Uzma Shah, Judy Squires, Véronique D. Morinville, Sohail Z. Husain, Maisam Abu‐El‐Haija

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2020
Typereview
Languageen
FieldMedicine
TopicPancreatitis Pathology and Treatment
Canadian institutionsMontreal Children's Hospital
Fundersnot available
KeywordsMedicineHepatologyPediatric gastroenterologyPancreatitisMagnetic resonance imagingRadiologyPosition paperAcute pancreatitisNeuroradiologyInterventional radiologyInternal medicineGeneral surgeryPathologyNeurology

Abstract

fetched live from OpenAlex

ABSTRACT: The reported incidence of pediatric pancreatitis is increasing. Noninvasive imaging, including ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI), play important roles in the diagnosis, staging, follow-up, and management of pancreatitis in children. In this position paper, generated by members of the Pancreas Committee of the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN) and the Abdominal Imaging Committee of The Society for Pediatric Radiology (SPR), we review the roles of noninvasive imaging in pediatric acute, acute recurrent, and chronic pancreatitis. We discuss available evidence related to noninvasive imaging, highlighting evidence specific to pediatric populations, and we make joint recommendations for use of noninvasive imaging. Further, we highlight the need for research to define the performance and role of noninvasive imaging in pediatric pancreatitis.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.017
GPT teacher head0.273
Teacher spread0.256 · 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 designNot applicable
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

Citations35
Published2020
Admission routes1
Has abstractyes

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