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Record W2123500228 · doi:10.5897/jmgg.9000024

HLA-DQA1 genotyping of Helicobacter pylori associated gastritis patients

2011· article· en· W2123500228 on OpenAlexaboutno aff
Nibras S. Al-Ammar, Ihsan Edan Alsaimary, Saad Sh. Hamadi, Ma Luo, Trevor Peterson, Chris Czarnecki

Bibliographic record

VenueJournal of Medical Genetics and Genomics · 2011
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsnot available
Fundersnot available
KeywordsGenotypingHelicobacter pyloriGastritisOdds ratioHuman leukocyte antigenInternal medicineMedicineGastroenterologyGenotypeAlleleLocus (genetics)Chronic gastritisImmunologyBiologyGeneticsGeneAntigen

Abstract

fetched live from OpenAlex

To study HLA-DQA1 genotyping in Helicobacter pylori associated gastritis patients. This study was carried out in College of Medicine, University of Basrah. HLA-DQA1 genotyping was done in College of Medicine, University of Manitoba, Winnipeg, Canada during the period from 17th of April 2009 to 15th of July 2010. A total of 100 patients (41 males and 59 females and a total of 30 controls (18 males and 12 females) were included in this study. DQA1 alleles frequencies were studied in 70H. pylori associated gastritis patients and 30 healthy controls. DQA1*0201 decreased allele frequency was statistically not significant in H. pylori associated gastritis patients, but there was a strong association (odds ratio= 2.61(, as compared with controls. In the present study many alleles from both locus showed high frequencies with a very strong association, but statistically not significant, this association has not been reported and it is important to note that a larger sample size should be studied.   Key words: HLA-DQA1 genotyping, DQA1*0201, Helicobacter

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)0.000
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.250
Teacher spread0.225 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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