End stage liver disease etiology & transplantation referral outcomes of major ethnic groups in British Columbia, Canada
Bibliographic record
Abstract
ABSTRACT: Liver disease etiology and transplantation outcomes may vary by ethnicity. We aimed to determine if disparities exist in our province.We reviewed the provincial database for liver transplant referrals. We stratified cohorts by ethnicity and analyzed disease etiology and outcomes.Four thousand nine hundred sixteen referrals included 220 South Asians, 413 Asians, 235 First Nations (Indigenous), and 2725 Caucasians. Predominant etiologies by ethnicity included alcohol (27.4%) and primary sclerosing cholangitis (PSC) (8.8%) in South Asians, hepatitis B (45.5%) and malignancy (13.9%) in Asians, primary biliary cholangitis (PBC) (33.2%) and autoimmune hepatitis (AIH) (10.8%) in First Nations, and hepatitis C (35.9%) in Caucasians. First Nations had lowest rate of transplantation (30.6%, P = .01) and highest rate of waitlist death (10.6%, P = .03). Median time from referral to transplantation (268 days) did not differ between ethnicities (P = .47). Likelihood of transplantation increased with lower body mass index (BMI) (hazard ratio [HR] 0.99, P = .03), higher model for end stage liver disease (MELD) (HR 1.02, P < .01), or fulminant liver failure (HR 9.47, P < .01). Median time from referral to ineligibility status was 170 days, and shorter time was associated with increased MELD (HR 1.01, P < .01), increased age (HR 1.01, P < .01), fulminant liver failure (HR 2.56, P < .01) or South Asian ethnicity (HR 2.54, P < .01). Competing risks analysis revealed no differences in time to transplant (P = .66) or time to ineligibility (P = .91) but confirmed increased waitlist death for First Nations (P = .04).We have noted emerging trends such as alcohol related liver disease and PSC in South Asians. First Nations have increased autoimmune liver disease, lower transplantation rates and higher waitlist deaths. These data have significance for designing ethnicity specific interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".