Model for end-stage liver disease and Child-Turcotte-Pugh score as predictors of pretransplantation disease severity, posttransplantation outcome, and resource utilization in United Network for Organ Sharing status 2A patients
Bibliographic record
Abstract
The Model for End-Stage Liver Disease (MELD) has been proposed as a replacement for the Child-Turcotte-Pugh (CTP) classification to stratify patients for prioritization for orthotopic liver transplantation (OLT). Improved classification of patients with decompensated cirrhosis might allow timely OLT before the development of life-threatening complications, reducing the number of critically ill patients listed as United Network for Organ Sharing (UNOS) status 2A at the time of OLT. We compared the ability of the MELD and CTP scores to predict pre-OLT disease severity, as well as outcome and resource utilization post-OLT. Data from 42 consecutive UNOS status 2A patients undergoing OLT at a single center were used to calculate MELD and CTP scores at the time of status 2A listing. Multivariate analysis was used to determine the relationship between these scores and pre-OLT disease severity measures, survival post-OLT, and measures of resource use post-OLT. The MELD was superior to CTP score in predicting pre-OLT requirements for mechanical ventilation and dialysis. Neither score correlated with the resource utilization parameters studied. Only two patients died within 3 months post-OLT; neither score was predictive of survival in this cohort. In summary, the MELD is superior to CTP score in estimating pre-OLT disease severity in UNOS status 2A patients and thus may help risk stratify status 2A or decompensated status 2B OLT candidates and optimize the timing of OLT. However, neither score correlated with resource use post-OLT in the strata of critically ill patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".