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Record W2297375826 · doi:10.1093/infdis/jiw060

<i>Helicobacter pylori</i>CagL Hypervariable Motif: A Global Analysis of Geographical Diversity and Association With Gastric Cancer

2016· article· en· W2297375826 on OpenAlexaff
Rebecca J. Gorrell, Nicolas Zwickel, John Reynolds, Dieter Bulach, Terry Kwok

Bibliographic record

VenueThe Journal of Infectious Diseases · 2016
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsHelicobacter pyloriHypervariable regionBiologyGeneticsCancerHelicobacterDiseaseEvolutionary biologyImmunologyMedicineGenePathology

Abstract

fetched live from OpenAlex

Previous studies suggest overrepresentation of particular polymorphisms within the Helicobacter pylori CagL hypervariable motif (CagLHM) in gastric cancer-associated isolates. However, these disease correlations were geographically variable and ambiguous. We compared the disease correlation of several hundred geographically diverse CagL sequences and identified 33 CagLHM sequence combinations with disparate geographical distribution, revealing substantial worldwide CagLHM diversity, particularly within Asian countries. Notably, polymorphisms E59 and I60 were significantly overrepresented, whereas D58 and E62 were underrepresented, in gastric cancer-associated H. pylori isolates worldwide. Thus, CagLHM regional diversity may contribute to the varied prevalence of H. pylori-related gastric cancer observed in diverse populations.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.229
Teacher spread0.222 · 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 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

Citations23
Published2016
Admission routes1
Has abstractyes

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