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Record W3135558749 · doi:10.1093/jcag/gwab002.206

A208 PREDICTORS OF OUTCOMES IN PSC: RETROSPECTIVE ANALYSIS OF TWO TERTIARY CARE CENTERS IN BC

2021· article· en· W3135558749 on OpenAlexaff
Daljeet Chahal, Harjot Bedi, Vladimir Marquez, Eric M. Yoshida, Hin Hin Ko, B Salh

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2021
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePrimary sclerosing cholangitisInternal medicineLiver transplantationPancolitisVedolizumabGastroenterologyInflammatory bowel diseaseClinical endpointProportional hazards modelRetrospective cohort studyUnivariate analysisDiseaseMultivariate analysisClinical trialTransplantationCancerColorectal cancerColonoscopy

Abstract

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Abstract Background Primary sclerosing cholangitis (PSC) is a chronic inflammatory disorder of the bile ducts. PSC can rapidly progress to cholangiocarcinoma and death. Many clinical features of PSC, as well as its relationship with diseases such as IBD, remain ill-defined. These features are important for disease modeling and clinical trial design. Aims To identify features of PSC that may aid in disease modeling and outcomes prediction. Methods Patients with a diagnosis of PSC with visits between 2012 and 2018 were identified and data were extracted. Survival analysis was performed, with time defined as time of PSC diagnosis to time at clinical endpoint. The clinical endpoint for survival analysis was defined as development of cholangiocarcinoma, liver transplantation or death. Univariate and multivariate Cox-regression was then performed. Results 169 patients (99 male, 70 female) were identified. Of these, 102 (60.4%) had a diagnosis of IBD (84 UC). 138 were Caucasian, 9 East Asian, 9 South Asian and 13 Middle East. Mean age at PSC diagnosis was 39.3, IBD diagnosis 29.3 years. Mean time to next diagnosis in those with PSC-IBD was 7.7 years. Of those with PSC-IBD, IBD preceded the diagnosis of PSC in 69 (67.6%) patients. 22 (13.0%) had concurrent liver disease, including 14 AIH and 1 PBC overlap. In those with UC, disease was most often pancolitis (57.8%), with noticeable rate of backwash ileitis (23.3%). There were 26 patients with current or prior use of Infliximab, 14 with Humira, and 6 with Vedolizumab. 28 (16.6%) patients had a partial or total colectomy. 35 (20.7%) patients had diagnoses of cancer, including 16 cholangiocarcinoma, 2 gall bladder carcinoma, and 4 colorectal. 33 (19.5%) patients received liver transplant, and 31 (18.3%) died. Most frequent cause of death was cholangiocarcinoma (12, 38.7%). Univariate analysis identified increased age at PSC diagnosis, presence of IBD, increased age at IBD diagnosis, diagnosis of IBD prior to PSC, increased time from diagnosis of IBD to PSC, diagnosis of UC as opposed to Crohn’s, and lack of Infliximab use as significant predictors of our clinical endpoints (p<0.05). Multivariate analysis only identified increased age at PSC diagnosis, presence of IBD, and diagnosis of IBD prior to PSC as predictors. Conclusions PSC affects persons of various ethnic backgrounds. Diagnosis of IBD appears to precede PSC in most PSC-IBD cases, and the temporal relationship may impact outcomes, possibly due to delayed diagnosis of PSC. UC has a worse disease course than Crohn’s. Cholangiocarcinoma still accounts for a large burden of overall death in PSC, and strategies for early diagnosis should be explored. More studies are required to delineate the relationship between biologic use and PSC outcomes. The major limitation of our study is the smaller sample size that may have limited statistical power. Funding Agencies NoneNone

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 score0.172
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.239
Teacher spread0.234 · 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
Published2021
Admission routes1
Has abstractyes

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Same venueJournal of the Canadian Association of GastroenterologySame topicLiver Diseases and ImmunityFrench-language works237,207