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Record W4323350856 · doi:10.1093/jcag/gwac036.181

A181 HIGHER CUMULATIVE HISTOLOGIC INFLAMMATORY BURDEN SCORE IS ASSOCIATED WITH THE RISK OF DEVELOPMENT OF COLORECTAL NEOPLASIA IN ULCERATIVE COLITIS

2023· article· en· W4323350856 on OpenAlexaff
D Graff, Cristian Hernández-Rocha, Krzysztof Borowski, J Stempak, James Conner, Mark S. Silverberg

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsSinai Health SystemUniversity of TorontoLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsMedicineUlcerative colitisInterquartile rangeInternal medicineGastroenterologyDysplasiaColonoscopyCohortColitisColorectal cancerPrimary sclerosing cholangitisInflammatory bowel diseaseCancerDisease

Abstract

fetched live from OpenAlex

Abstract Background Ulcerative colitis (UC) patients have an elevated risk of colorectal neoplasia (CRN). Younger age at diagnosis, extent of colitis, and longer duration of colitis, as well as increased severity, which is a component of the cumulative inflammatory burden score (CIB), have been associated with the development of CRN. CIB was developed based on a large cohort of UC patients from St. Mark’s Hospital (UK) but needs further validation in independent cohorts. Purpose We analyzed the association between higher histologic CIB and development of CRN in longstanding UC patients. Method A matched case-control cohort of UC patients with at least 8 years of disease duration was analyzed at Mount Sinai Hospital. Patients with primary sclerosing cholangitis were excluded. Cases consisted of UC patients with colitis-associated neoplasia defined as indefinite for dysplasia (IND), low-grade dysplasia (LGD), high-grade dysplasia (HGD), or colorectal cancer (CRC). Each case was matched to two controls by age at disease onset, disease duration, and histological extent of colitis. Histologic reports obtained by colonoscopy were reviewed and histological activity was assessed as quiescent/normal (0), mild (1), moderate (2), and severe (3). The colonic area with the higher score was utilized and the CIB was calculated by summing each score and multiplying it by the interval of surveillance. A mean CIB (mCIB) was also calculated for each patient dividing the CIB by the number of colonoscopies. Continuous variables including CIB scores and mCIB scores were summarized as median and interquartile range (IQR) and differences between groups were compared by Mann-Whitney test. Result(s) Fifty-four UC patients were analyzed with 18 having CRN (6 CRC, 2 HGD, 3 LGD and 7 IND) and 36 controls without CRN. The clinical characteristics of the total cohort, cases and controls are depicted in the Table. Median age at last colonoscopy assessed was 45 years (36-55) and 40.7% were female. The median age at onset of UC was 23 years (19-37) and median duration of UC was 16 years (11-23). All patients had extensive histologic colonic disease. There were no differences between cases and controls in interval of surveillance evaluated (7.5 vs 7.8 years, p = 0.7) and median number of colonoscopies with histologic assessment (4 vs 4, p =0.6). Cases with CRN had significantly higher CIB (11.4 vs 7.9, p = 0.02) and mCIB (2.9 vs 2.0, p = 0.02) compared to controls. Image Conclusion(s) The histologic CIB score is associated with an increased risk of developing CRN in UC patients with similar age at onset of disease, disease duration and colitis extent. Given CIB reflects the severity of histologic inflammation over the years, treatment strategies to improve histologic inflammation could reduce the incidence of CRN in UC. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

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)0.002
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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
Published2023
Admission routes1
Has abstractyes

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