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Record W2352509074

The Vienna versus the Montreal classification for Crohn's disease

2010· article· en· W2352509074 on OpenAlexaboutno aff
LU Fang-ge

Bibliographic record

VenueShiyong yixue zazhi · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
Fundersnot available
KeywordsCrohn's diseaseMedicineDiseaseComplicationIleumGastroenterologyInflammatory bowel diseaseInternal medicineSurgery
DOInot available

Abstract

fetched live from OpenAlex

Objective To investigate the phenotypic clinical expression of Crohn's disease in Chinese people using the Vienna and the Montreal classification. Methods We retrospectively analyzed the clinical data of 168 patients with Crohn's disease,then typed the disorder according to the Vienna and the Montreal classification,and then compare the features of each subtype. Results On the Montreal classification,patients with A1 subtype (only 5.4%) were at the age of less than or equal to 16,while most patients (63.1%) were 17 to 40,in consistency with the rate of the patients under 40 (65.5%) on the Vienna classification. Disease was mainly localized in the ileum and the ileocolon. Fewer patients with L4 subtype were found on the Montreal than on the Vienna classification (7.1% vs.13.7% ,P = 0.049). Stricture formation was the major complication (40.5% on the Vienna and 41.7% on the Montreal),and penetration was less on the Montreal than on the Vienna classification (10.7% vs. 18.5%,P = 0.044). The surgery rate for penetrating behavior was much higher on the Montreal than on the Vienna classification (88.9% vs. 54.8%,P = 0.014). Conclusions In China,Crohn's disease is rare in adolescents and occurs mainly in people at the age of 17 to 40. The disease is mainly localized in the ileum and the ileocolon. Stricture formation is a common complication and penetration is the major cause of surgery. Both the Vienna and the Montreal classification have their own advantages in typing Crohn's disease and thus combined application of the two classifications is more valuable.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.259
Teacher spread0.247 · 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.

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

Citations0
Published2010
Admission routes1
Has abstractyes

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