Evolving trends in liver transplantation eligibility: A shift toward inclusivity for older adults at a Canadian Transplant Center
Bibliographic record
Abstract
Background: The surge of end-stage liver disease among older individuals challenges traditional age-based criteria for liver transplantation (LT), historically capped at 65 years. Our Canadian center shifted away from using chronologic age as an absolute refusal criterion since 2019, enabling those aged 65 years and older to seek LT. This study aimed to investigate temporal trends in the transplant care cascade for patients aged 65 and older at our center, pre- and post-clinical shift. Methods: A retrospective study in a single Canadian transplant center reviewed LT referrals between 2015 and 2023, analyzing proportions of patients aged 65 and above at each stage. Specific intervals, 2015–2018 and 2019–2023, were defined for pre- and post-comparisons. Results: Among the 1,007 LT referrals, 11% (n = 110) were patients aged ≥65 years, with 74% ( n = 81) of them being referred after 2019. From 2015 to 2023, older patient proportions increased at all stages of the transplant care cascade: referrals (7.4% to 12.6%), evaluations (7.6% to 11.4%), waitlisting (5.6% to 15.4%), and transplantations (5.8% to 17.5%). Post-clinical shift, the proportion of older patients referred nearly doubled (7.5% vs 13.7%; p < 0.05), with a similar increase in transplants (5.7% vs. 11.5%; p < 0.05). Conclusions: Removing the age cap increased older patient engagement in the LT care cascade. This emphasizes the crucial role of actively promoting awareness of evolving LT eligibility criteria. Concerted efforts should focus on improving transplantation accessibility in older patients, ensuring age alone does not impede the process.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".