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Record W2087651118 · doi:10.1111/apa.13026

Anti‐<i><scp>S</scp>accharomyces cerevisiae</i> antibody titres correlate well with disease activity in children with <scp>C</scp>rohn's disease

2015· article· en· W2087651118 on OpenAlexaff
Wael El‐Matary, Karine Dupuis, AbdulRazaq Sokoro

Bibliographic record

VenueActa Paediatrica · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
Fundersnot available
KeywordsMedicineAntibodyCrohn's diseaseImmunoglobulin AImmunologyDiseaseInflammatory bowel diseaseAlbuminC-reactive proteinImmunoglobulin GSerum albuminUnivariate analysisInternal medicineGastroenterologyInflammationMultivariate analysis

Abstract

fetched live from OpenAlex

AIM: The role of noninvasive biologic markers for disease activity is very important in children with Crohn's disease. The aim of this study was to assess an association between disease activity and quantitative serum anti-Saccharomyces cerevisiae antibody (ASCA) titres. METHODS: Anti-Saccharomyces cerevisiae antibody immunoglobulin (Ig) A and immunoglobulin G titres, paediatric Crohn's disease activity index (PCDAI), serum albumin and C-reactive protein (CRP) were repeatedly measured simultaneously in children with Crohn's disease. A possible association between ASCA IgA and IgG titres and changes in PCDAI was examined. RESULTS: Serial 136 measurements of ASCA IgA and IgG titres were documented in 57 children with Crohn's disease over a mean duration of 3.1 ± 2.1 years. In a univariate linear regression model, there were significant correlations between ASCA IgA titres and PCDAI (p < 0.001), CRP (p <0.01) and low serum albumin (p < 0.001), respectively. Similarly, ASCA IgG titres significantly correlated with PCDAI, CRP and low serum albumin. CONCLUSION: Both ASCA IgA and IgG titres seemed to correlate well with clinical Crohn's disease activity in children. Measuring these antibodies may be considered during routine clinical care for those patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.216
Teacher spread0.210 · 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 teacher head, not a consensus.

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

Citations10
Published2015
Admission routes1
Has abstractyes

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