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Record W4323350992 · doi:10.1093/jcag/gwac036.252

A252 LONG-TERM OUTCOMES IN INFLAMMATORY BOWEL DISEASE PATIENTS WITH PRIMARY SCLEROSING CHOLANGITIS: KINGSTON HEALTH SCIENCES CENTER LOCAL EXPERIENCE

2023· article· en· W4323350992 on OpenAlexaffabout
M Greenblatt, D Mulder, Jennifer A. Flemming

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicinePrimary sclerosing cholangitisInflammatory bowel diseaseRetrospective cohort studyPopulationCohortInternal medicineIncidence (geometry)DiseaseColorectal cancerUlcerative colitisCohort studyCancerEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background IBD-PSC remains a poorly understood entity due to it’s low incidence and the heterogeneity found within this patient population. Patients with IBD-PSC have 0.3-2.8% lifetime risk of developing hepatocellular carcinoma, up to a 20% lifetime risk of developing Cholangiocarcinoma and a 20-30% lifetime risk of developing Colorectal Cancer (Fung et al. World J Gastroenterol 2019). To better understand long-term outcomes in this population, the use of large population level data can be useful but requires case validation. Purpose A large multicenter Canadian retrospective cohort study is ongoing with the goals of identifying an algorithm to identify IBD-PSC patients and compare long-term outcomes of IBD-PSC patients with a matched IBD cohort (Ricciuto et al, ongoing). This abstract describes the local data within Kingston Health Sciences Centre (KHSC) from this Canadian multi-center study and evaluates the use of ICD codes for the identification of IBD-PSC. Method This is a single centre retrospective cohort study of patients with IBD-PSC within KHSC from January 1993 - December 2021. Patients with potential IBD-PSC were initially identified locally using ICD codes for IBD and cholangitis. Charts were reviewed and IBD-PSC diagnosis was confirmed using pathology from liver biopsies and imaging. Those confirmed to have IBD-PSC then had data manually extracted from the local health administrative system. Outcomes of interest were divided into patient demographics, disease phenotype at diagnosis of IBD-PSC and therapies. Patients were followed until death, leaving the KHSC network or the end of the follow-up period. Result(s) Of the 862 patients identified using ICD codes, 16 (2%) were confirmed to have IBD-PSC after chart review. 50% of included patients had other autoimmune diseases. The median age of diagnosis of PSC was 34 years (IQR=22-43) while the median age of diagnosis of IBD was 22 (IQR=15-38). Large duct PSC was found in 88% of patients. Six patients had disease that was exclusively intra-hepatic. Two patients were found to have PSC with auto-immune hepatitis overlap. Mean ALP at diagnosis was 402 (IQR=208-506). Four patients developed cirrhosis, two of which experienced hepatic decompensation. Three patients underwent liver transplant. 81% of patients had ulcerative colitis. At the index lower endoscopy, 33% of patients had Mayo 3 colitis and 75% had involvement of the right colon. During follow up in our study, 81% of patients required systemic steroids and 33% required biologic therapy. Conclusion(s) ICD codes alone cannot reliably identify patients with IBD-PSC. The characteristics of the patients in our local experience are in keeping with existing literature. Completion of this multi-center study will allow for a greater sample size, a matched control group and a better understanding of the long-term impacts of this disease. 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)0.001
metaresearch head score (Gemma)0.003
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 score0.333
Threshold uncertainty score0.671

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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