Recipient age influences survival after liver transplant: Results of the French national cohort 2007–2017
Bibliographic record
Abstract
BACKGROUND: In recent years, age at liver transplantation (LT) has markedly increased. In the context of organ shortage, we investigated the impact of recipient age on post-transplantation mortality. METHODS: All adult patients who received a first LT between 2007 and 2017 were included in this cross-sectional study. Recipients' characteristics at the time of listing, donor and surgery data, post-operative complications and follow-up of vital status were retrieved from the national transplantation database. The impact of age on 5-year overall mortality post-LT was estimated using a flexible multivariable parametric model which was also used to estimate the association between age and 10-year net survival, accounting for expected age- and sex-related mortality. RESULTS: Among the 7610 patients, 21.4% were aged 60-65 years, and 15.7% over 65. With increasing age, comorbidities increased but severity of liver disease decreased. Older recipient age was associated with decreased observed survival at 5 years after LT (p < .001), with a significant effect particularly during the first 2 years. The linear increase in the risk of death associated with age does not allow any definition of an age's threshold for LT (p = .832). Other covariates associated with an increased risk of 5-year death were dialysis and mechanical ventilation at transplant, transfusion during LT, hepatocellular carcinoma and donor age. Ten-year flexible net survival analysis confirmed these results. CONCLUSION: Although there was a selection process for older recipients, increasing age at LT was associated with an increased risk of death, particularly in the first years after LT.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".