Postoperative Resource Utilization and Survival among Liver Transplant Recipients with Model for End-Stage Liver Disease Score ≥40: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Cirrhotic patients with Model for End-stage Liver Disease (MELD) score ≥ 40 have high risk for death without liver transplant (LT). OBJECTIVE: To evaluate these patients' outcomes after LT. METHODS: The present study analyzed a retrospective cohort of 519 cirrhotic adult patients who underwent LT at a single Canadian centre between 2002 and 2012. Primary exposure was severity of liver disease measured by MELD score at LT (≥ 40 versus < 40). Primary outcome was duration of first intensive care unit (ICU) stay after LT. Secondary outcomes were duration of first hospital stay after LT, rate of ICU readmission, re-LT and survival rates. RESULTS: On the day of LT, 5% (28 of 519) of patients had a MELD score ≥ 40. These patients had longer first ICU stays after LT (14 versus two days; P < 0.001). MELD score ≥ 40 at LT was independently associated with first ICU stay after LT ≥ 10 days (OR 3.21). These patients had longer first hospital stays after LT (45 versus 18 days; P < 0.001); however, there was no significant difference in the rate of ICU readmission (18% versus 22%; P = 0.58) or re-LT rate (4% versus 4%; P = 1.00). Cumulative survival at one month, three months, one year, three years and five years was 98%, 96%, 90%, 79% and 72%, respectively. There was no significant difference in cumulative survival stratified according to MELD score ≥ 40 versus < 40 at LT (P = 0.59). CONCLUSIONS: Cirrhotic patients with MELD score ≥ 40 at LT utilize greater postoperative health resources; however, they derive similar long-term survival benefit from 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".