Clinical epidemiological analysis of the mortality rate of liver transplant candidates living in rural areas
Bibliographic record
Abstract
MELD score has been used to predict 90-day mortality of subjects listed for liver transplantation (OLT). Validation of MELD score for patients on the waiting list in transplant programmes serving rural areas in North America is lacking. A retrospective cohort of patients affected by end-stage liver disease was studied to assess the mortality rate within 90 days after being listed at our transplant centre. Secondary aims were to identify differences between predicted and observed 90-days mortality using MELD and MELDNa scores at the time of listing. Among 126 patients included in this study, waiting list mortality was 35.0%. Ninety-day mortality was 21.1%, which was significantly greater than the mortality estimated by the MELD (9.1%, 95% CI: 6.6-11.5) and MELDNa (9.3%, 95%CI: 6.0-12.5). Despite this underestimation, AUC for MELD and MELDNa was 0.80 and 0.78 respectively. In our study, independent predictors of waiting list mortality were age, diagnosis of cholestatic disease and residence over 500 km from our transplant centre. MELD and MELDNa underestimated the 90-day mortality in patients with liver failure living in rural areas. Validation of these models should be performed in other transplant centres serving patients with limited access to specialized services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".