Recipient ineligibility after liver transplantation assessment: a single centre experience
Bibliographic record
Abstract
BACKGROUND: Candidacy for liver transplantation is determined through standardized evaluation. There are limited data on the frequency and reasons for denial of transplantation after assessment; analysis may shed light on the short-term utility of the assessment. We sought to describe the frequency and reasons for ineligibility for liver transplantation among referred adults. METHODS: We studied all prospectively followed recipient candidates at a single centre who were deemed unsuitable for liver transplantation after assessment. Inclusion criteria were age 18 years and older and completion of a standard liver transplantation evaluation over a 3-year period. Patients were excluded if they had a history of prior assessment or liver transplantation within the study period. Demographic and baseline clinical data and reasons for recipient ineligibility were recorded. RESULTS: In all, 337 patients underwent their first liver transplantation evaluation during the study period; 166 (49.3%) fulfilled inclusion criteria. The mean age was 55.4 years, and 106 (63.9%) were men. The 3 most common reasons for denial of listing were patient too well (n = 82, 49.4%), medical comorbidities and/or need for medical optimization (n = 43, 25.9%) and need for addiction rehabilitation (n = 28, 16.9%). CONCLUSION: Ineligibility for transplantation after assessment was common, occurring in nearly half of the cohort. Most denied candidates could be identified with more discriminate screening before the resource-intensive assessment; however, the assessment likely provides unforeseen positive impacts on patient care.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".