A89 LIVER TRANSPLANT REFERRAL PATTERNS FROM THE ATLANTIC CANADIAN PROVINCES
Bibliographic record
Abstract
Abstract Background End-stage liver disease (ESLD) is one of the leading causes of deaths in Canada. Liver transplantation (LT) is the ultimate treatment option for patients with ESLD. A variety of factors may influence patient outcomes before a liver transplant is offered. There have been no studies looking at the characteristics of patients referred for LT in Atlantic Canada at the prelisting and wait-listing stages. Aims To examine various patient characteristics, including age, sex, MELD score, BMI, rural vs. urban residency, and diagnosis that may be important to the LT work up process. To better understand referral patterns and potential barriers to LT in Atlantic Canada including geographic factors, the COVID-19 pandemic, and the evolution of underlying liver disease. Methods This is a retrospective cohort study using Multiorgan Transplant (MOTP) database to identify all active referrals sent to the Atlantic liver transplant program from four Atlantic Canadian provinces between January 1, 2017 and September 30, 2023. Results There were 533 LT referrals to the MOTP in the observation period. From the listed patients (217), 60% were transplanted and 25% died on the waiting list. Of the patients that did not progress to being listed (56%), 197 (37%) of the referrals were withdrawn and 99 (18.6%) died prior to being listed. There was no significant difference in mean BMI, age, listing MELD-Na, or diagnosis (p=0.35) among active referrals between pre-COVID-19 (Jan.1 2017-Mar.30 2020) and COVID-19 periods (Apr.1 2020-Sept.30 2023). There was 6% reduction in referral number from all Atlantic provinces in COVID vs. pre-COVID period, and 25% reduction in referrals for hepatocellular carcinoma (HCC) in COVID. The ratio of patients ampersand:003C 65 yrs to ampersand:003E/= 65 yrs decreased during COVID (2.88) compared to pre-COVID (3.57). For pre and post COVID period, there were similar numbers of patients withdrawn/died prior to listing (54.7%, 54.8%). There were less patients listed (43.0%, 38.2%) and a decrease in patients transplanted (32%, 28%) pre and post COVID. We analyzed the number of referrals from urban and rural areas. Overall, 33% of referrals were urban and 67% of referrals were rural based on patient residence. The ratio of rural to urban area referrals was 1.80 pre COVID and 2.24 during Covid, indicating an increasing number of referrals from rural centers post-COVID. Conclusions Although there was a robust number of LT referrals during the pandemic, there was a decrease in number of referrals and transplants after COVID. There is a notable decrease in referrals for HCC after COVID. One plausible reason is reduced HCC surveillance during the pandemic. There was an increase in referrals from rural areas during COVID. Further analysis will be needed to determine any factors that affect LT referral patterns., with the goal of identifying reasons for this change and making appropriate recommendations to the MOTP. Funding Agencies None
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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".