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Record W1683405372 · doi:10.1053/j.gastro.2015.07.040

American Gastroenterological Association Institute Technical Review on the Role of Upper Gastrointestinal Biopsy to Evaluate Dyspepsia in the Adult Patient in the Absence of Visible Mucosal Lesions

2015· review· en· W1683405372 on OpenAlexaff
John I. Allen, David A. Katzka, Marie E. Robert, Grigorios I. Leontiadis

Bibliographic record

VenueGastroenterology · 2015
Typereview
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineBarrett's esophagusEsophagusEndoscopyEsophagogastroduodenoscopyEndoscopic mucosal resectionGastroenterologyInternal medicineBiopsyGuidelineGeneral surgeryPathologyCancer

Abstract

fetched live from OpenAlex

AGA SECTIONPopulation.This Technical Review is focused on patients older than 18 years of age who present for endoscopy with dyspepsia and no alarm symptoms (eg, dysphagia, anemia, bleeding, and weight loss) combined with normal-appearing mucosa on EGD.Erythema was classified as normal appearance of the mucosa.In studies that reported outcomes for patients with erythema separately from those with normal appearance, we reclassified erythema as "normal" and recalculated outcomes accordingly.Intervention.The intervention for each question is routine tissue acquisition and analysis.A secondary intervention (Question #2) would be the use of any special stain beyond routine H&E staining.Comparator.The intervention (biopsy) is compared with no biopsy.The use of special stains is compared with restricting histologic examination to H&E staining only.Outcome.If biopsies are sent, how accurately is the disease of interest identified in the absence of gross endoscopic findings?What is the likelihood of changing health outcomes of October

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)0.004
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.041
GPT teacher head0.339
Teacher spread0.298 · 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

Citations54
Published2015
Admission routes1
Has abstractyes

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