S1196 Disparities in Performance of the Model for End-Stage Liver Disease Score Across Different Races and Ethnicities
Bibliographic record
Abstract
Introduction: The prevalence of cirrhosis has doubled in the past decade, and it became the 11th leading cause of death in the United States in 2017. The Model for End-Stage Liver Disease (MELD) score stratifies the severity of end-stage liver disease and is used to prioritize liver transplantation. It has also been validated as a predictor of in-hospital mortality for patients with cirrhosis. Despite the score’s widespread adoption, there has been little research on the impact of the patient’s race-ethnicity on the score’s performance. Methods: The performance of the MELD score in predicting in-hospital mortality for the Asian, African American, Hispanic, and White patient groups was compared for 11,326 admission records in the Medical Information Mart for Intensive Care IV database for patients with cirrhosis. The binned 90-days mortality rates proposed by Wiesner et al. [1] were used to determine the comparative MELD scores. Discrimination and calibration were determined for the score in all four groups. Discrimination of the MELD score was determined by the area under the receiver operating characteristic (AUROC) curve, and calibration was evaluated using a standardized mortality ratio (SMR) between actual and predicted mortality ratios in each predicted mortality category. Results: The across-group difference in the AUROCs was statistically significant (p=0.01). In the subgroup comparisons, there was a separation between the Hispanic and White patients, but all other comparisons had overlapping 95% confidence intervals. Calibration showed that the overall SMR for African Americans was lower than for all ethnicities by 60.3%. A similar trend was observed in the analysis across mortality categories, where the SMRs for African Americans were lower than for all ethnicities across all categories. In addition, the SMRs for Hispanics were lower than for all ethnicities across three mortality categories (Table 1). Conclusion: The MELD score is commonly used to inform clinical decisions for patients with cirrhosis. Our analysis suggests racial disparities in the accuracy of its estimate of mortality among hospitalized patients. Further work is needed to identify the etiology of these disparities and ensure consistent performance of the score across all races and ethnicities.Table 1.: Outcomes of Infective Endodcarditis Patients stratified by Cirrhosis Status.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".