O-16 MELD-NA AND MELD3.0 HAVE THE BEST PERFORMANCE TO PREDICT THE 28-DAY RISK OF DEATH IN PATIENTS WITH SEVERE ALCOHOLIC HEPATITIS IN THE MEXICAN POPULATION
Bibliographic record
Abstract
Severe alcoholic hepatitis (AH) has a high mortality rate, and currently, it is still a challenge to be able to establish the prognosis of these patients and their risk of death at admission in order to be able to offer better therapeutic alternatives that save a life in a timely manner. This study aimed to compare several prognostic scores to verify which of them has the best performance in predicting 28-day mortality at admission in patients with AH. Observational, cohort study. Data were collected from patients with severe AH who were hospitalized between January 2010 to May 2022. MELD, MELDNa, MELD3.0, ABIC, Maddrey, Glasgow scale for AH were calculated with admission parameters, and their outcome was verified at 28 days. ROC curves were constructed to compare the different prognostic scales. 144 patients were included, 129 (89.6%) men, mean age 43.3±9.3 years, median grams of alcohol consumed/day were 320 (range: 60-1526). 65 (45.1%) died. The mean of MELD, MELDNa and MELD3.0 were higher among the deceased vs. survivors (33.5±7.5 vs. 27.1±6.2; 34.6±5.7 vs. 29.1±5.7; and 35.8±6.0 vs. 30.1±5.5 respectively; p<0.0001). The ROC curve analysis comparing the prognostic scales is shown in Figure 1. AH mortality is high. MELDNa and MELD3.0 have the best performance for predicting on admission which patients with AH are at risk of dying in the next 28 days and can be useful tools for prioritizing patients who will require life-saving strategies, such as liver transplantation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".