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Record W2001829069 · doi:10.1159/000338147

Towards a Novel Molecular Classification of IBD

2012· review· en· W2001829069 on OpenAlexaboutno aff
Séverine Vermeire

Bibliographic record

VenueDigestive Diseases · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineUlcerative colitisDiseaseInflammatory bowel diseaseCrohn's diseaseInternal medicineBioinformatics

Abstract

fetched live from OpenAlex

Classifying IBD patients is important for decisions on the intensity of follow-up, therapy and mode of delivery. The most recent classification of Crohn's disease (CD) and ulcerative colitis (UC), the Montreal classification, is based on clinical grounds: for CD, the age at diagnosis and disease location and behavior and for UC, the age at diagnosis and the extent of disease. During the Working Party in 2005 in Montreal, it was judged by the panel of experts that a molecular reclassification using serology and/or genetic markers was too premature and not yet justified. In the meanwhile, the number of genes associated with IBD has increased to >100 and more antimicrobial peptides have also been identified. We recently showed that genetic variants enable the classification of CD patients in distinct clusters, which is different from clusters seen in healthy individuals. Furthermore, there was a poor relationship between the genetic-based subgroups and the clinical subphenotypes being used. The promising role for molecular markers lies most likely in disease stratification, the prediction of prognosis at the time of diagnosis and the prediction of therapy outcome. The behavior of CD and UC varies between patients, and the characteristic transmural inflammation in CD--when untreated--will often progress and lead to stricture or fistula formation over time. Predicting which patients are at risk for progression to complications and how fast this occurs could have therapeutic implications, as more aggressive treatment strategies could become defendable in the appropriate patient. Genetic factors are definitively more appealing for risk stratification on more solid grounds. Molecular markers may also better explain changes in disease behavior from a pathogenic standpoint, and by combining several markers this strategy can approach clinical utility. Before starting to apply molecular markers to predict disease behaviors, tools should be made available to score disease progression. Only then could the value of molecular markers to predict the speed of progression be fully explored in prospective studies.

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)0.005
metaresearch head score (Gemma)0.009
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: none
Teacher disagreement score0.006
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.002
Science and technology studies0.0010.002
Scholarly communication0.0050.005
Open science0.0020.002
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0020.004

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 head0.038
GPT teacher head0.316
Teacher spread0.279 · 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

Citations19
Published2012
Admission routes1
Has abstractyes

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