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Record W4323351030 · doi:10.1093/jcag/gwac036.044

A44 A 1000 PATIENT CANADIAN NETWORK FOR AUTOIMMUNE LIVER DISEASE EVALUATION OF CLINICAL AND DEMOGRAPHIC PATTERNS OF AUTOIMMUNE HEPATITIS

2023· article· en· W4323351030 on OpenAlexaffabout
Christina Plagiannakos, Aldo J. Montaño‐Loza, Ellina Lytvyak, Jonelle Pallotta, Andrew L. Mason, K M Qumosani, Lawrence Worobetz, Jennifer A. Flemming, Julian Hercun, Catherine Vincent, Angela Cheung, T Chen, Dusanka Grbic, Mark G. Swain, Aliya Gulamhusein, Bettina E. Hansen, Gideon M. Hirschfield

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsMcGill University Health CentreUniversity of OttawaUniversity of CalgaryCentre Hospitalier de l’Université de MontréalUniversité de SherbrookeQueen's UniversityWestern UniversityUniversity of SaskatchewanUniversity of AlbertaUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineAutoimmune hepatitisCohortDemographicsInternal medicineCirrhosisLogistic regressionDiseaseConfoundingLiver diseasePediatricsDemography

Abstract

fetched live from OpenAlex

Abstract Background We sought to understand how the demographics of autoimmune hepatitis (AIH) have changed over time in Canada. Purpose Using a large multi-centre Canadian cohort of patients with AIH, we describe the trends in patient and disease characteristics at presentation across 30 years of clinical practice. Method Patients from the Canadian Network for Autoimmune Liver Disease with a confirmed diagnosis of AIH (simplified score ≥6) were included for analysis. Patients were grouped into five cohorts according to the year of diagnosis (i.e., <2000, 2000-2004, 2005-2009, 2010-2014, ≥2015). Patient demographics and baseline clinical and biochemistry features of disease activity were investigated using Chi-square tests and Kruskal-Wallis tests adjusted for multiple comparisons. Logistic and linear regression models with estimated means were utilized to further investigate relationships with time and to adjust for confounding. Result(s) 1016 patients followed across 10 Canadian health centres with AIH were diagnosed between November 1965 and December 2021. Overall, 76.4% (n=776) of patients were female, and the median age at diagnosis was 46 years (IQR 28.2 - 58.3). Cirrhosis at presentation was seen in 20.6% of patients (n=209). The median age at diagnosis increased significantly from 31.8 years [IQR 17.9 - 46.8] pre-2000 to 54 years [IQR 9.0 - 95.2] after 2014 (p<0.001; Figure 1a). This effect of time persisted after adjusting for sex and cirrhosis status at diagnosis. Female sex and the presence of cirrhosis at diagnosis were factors independently associated with older age at presentation (p<0.0001). The proportion of patients that presented with cirrhosis at diagnosis increased significantly over calendar time, from 13.7% (n=23) pre-2000 to 30.8% (n=69) after 2014 (p=0.003; Figure 1b). Male sex was independently associated with an increased odds of having cirrhosis at presentation (OR= 1.46, CI 1.02 - 2.07) and higher baseline ALT levels compared to females (p=0.036). The proportion of patients that identified as non-white ethnicity increased significantly from 15.2% (n= 24) pre-2000, to 32% (n= 86) after 2014 (p<0.001, Figure 1b). This effect of time on ethnicity was most pronounced after the year 2010 (OR= 2.32, CI 1.39 - 3.98) and persisted after adjusting for sex. There was no significant pattern of change in sex over calendar time. Image Conclusion(s) In Canada, patients with AIH at presentation are now older, have more advanced disease, and are more ethnically diverse than when compared to 30 years ago. Please acknowledge all funding agencies by checking the applicable boxes below Other Please indicate your source of funding; industry 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)0.001
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 score0.044
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.026
GPT teacher head0.286
Teacher spread0.260 · 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
Published2023
Admission routes2
Has abstractyes

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