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Record W4289028940 · doi:10.1097/pg9.0000000000000224

Advances in Pediatric Diagnostic Endoscopy

2022· review· en· W4289028940 on OpenAlexaff
Diana G. Lerner, Ali Mencin, Inna Novak, Clifton Huang, Kenneth Ng, Richard A. Lirio, Julie Khlevner, Elizabeth C. Utterson, Brendan R. Harris, Ryan Fong, Sabina Mir, Roberto Gugig, Catharine M. Walsh, Doug S. Fishman

Bibliographic record

VenueJPGN Reports · 2022
Typereview
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsSickKids FoundationThe Wilson CentreHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsEndoscopyCapsule endoscopyMedicineEndomicroscopyModalitiesRadiologyMedical physicsMedical imagingConfocal

Abstract

fetched live from OpenAlex

Pediatric endoscopy has revolutionized the way we diagnose and treat gastrointestinal disorders in children. Technological advances in computer processing and imaging continue to affect endoscopic equipment and advance diagnostic tools for pediatric endoscopy. Although commonly used by adult gastroenterologists, modalities, such as endomicroscopy, image-enhanced endoscopy, and impedance planimetry, are not routinely used in pediatric gastroenterology. This state-of-the-art review describes advances in diagnostic modalities, including image-enhanced endoscopy, confocal laser endomicroscopy, optical coherence tomography, endo functional luminal imaging probes, wireless motility/pH capsule, wireless colon capsule endoscopy, endoscopic ultrasound, and discusses the basic principles of each technology, including adult indications and pediatric applications, safety cost, and training data.

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)0.001
metaresearch head score (Gemma)0.002
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
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.032
GPT teacher head0.365
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 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

Citations15
Published2022
Admission routes1
Has abstractyes

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