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Record W2586694276 · doi:10.1093/ecco-jcc/jjx002.337

P212 Location and Kudo pit pattern reflect neoplastic histology of lesions detected at surveillance colonoscopy in inflammatory bowel disease

2017· article· en· W2586694276 on OpenAlexaff
Marietta Iacucci, Adetunmise Oluseyi, Remo Panaccione, Xianyong Gui, Stefan J. Urbanski, Parham Minoo, Gilaad G. Kaplan, Kerri L. Novak, Mark Lowerison, Brendan Cord Lethebe, Yvette Leung, Cynthia H. Seow, Subrata Ghosh

Bibliographic record

VenueJournal of Crohn s and Colitis · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChromoendoscopyMedicineDysplasiaColonoscopyInternal medicineInflammatory bowel diseaseGastroenterologyUnivariate analysisLesionOdds ratioColorectal cancerDiseaseMultivariate analysisPathologyCancer

Abstract

fetched live from OpenAlex

Background: Effective colonoscopic surveillance of IBD benefit from having reliable predictors of neoplasia, since targeted biopsies and endoscopic resection are increasingly used as standard of practice. It is not clear whether Kudo pit patterns may be applicable in characterizing IBD associated lesions. We aimed to identify the specific clinical and endoscopic features of colonic lesions which predict dysplasia in IBD. Methods: All lesions identified in a randomized study to determine the detection rates of neoplastic lesion (NL) in patients with long standing colitis in IBD (ClinicalTrials.gov NCT02098798) were included. Endoscopic NL were classified by the Paris classification and Kudo pit pattern, and by the Vienna classification histologically. Exploratory univariate analysis was performed, and age, duration of disease, extra-intestinal manifestations family or personal history of polyps/cancer, smoking, size of lesion, Paris classification, Kudo pattern, localisation/extension of disease were considered in the patients with IBD- associated NL. Subsequently a multivariate logistic regression model analyses was created with candidate variables which had p values ≤0.05 based on univariate analysis Results: A total of 270 patients (55% men; age 20–77y) were assessed by High Definition – white light (n=90), virtual chromoendoscopy (n=90) or dye chromoendoscopy (n=90). Ninety-one (33.7%) colonic dysplastic lesions and 1 adenocarcinoma were found. Sixty-two (68.8%) were polypoid and twenty-nine (31.8%) were non polypoid. Most of these lesions (92.3%) had Kudo pattern III–V (Table). By univariate analysis, age – Odds Ratio (OR) 1.05 (95% CI: 1.02–1.08), localization of the lesions in the right colon – OR 6.15 (95% CI: 3.12–12.12), Kudo pattern IIO, III-IV and V – OR 20.91 (95% CI: 9.34–46.7) and Paris Is/Ip classification OR – 3.29 (95% CI: 1.69–6.38) were associated with NL. Subsequently proportional multivariate logistic regression model for the prediction of colonic neoplasia confirmed that the endoscopic Kudo pit pattern – OR 21.50 (95% CI: 86.5–60.1) and localization of the lesions in the right colon – OR 6.52 (95% CI: 1.98–22.5) were predictors of colonic neoplasia at surveillance colonoscopy in IBD (Table). The overall accuracy of independent variables which predict neoplastic changes was 78% (95% CI 68–88%), sensitivity 82% (95% CI 68–97%), specificity 68% (95% CI 47–89%), PPV 85% (95% CI 76–95%) and NPV64% (95% CI 42–86%) which were significant in the multivariate analysis. Conclusions: We demonstrated that the endoscopic Kudo pattern and localisation of the lesions in the right colon were predictors of colonic neoplasia in IBD. This may guide management strategy of NL detected at IBD surveillance.

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)0.004
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.007
GPT teacher head0.250
Teacher spread0.243 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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