Risk Factors for Liver Cirrhosis-Related Readmissions in the Largest Ethnic Minority in United States
Bibliographic record
Abstract
Background: There are very limited data available on 30-day readmissions for ethnic minority patients with cirrhosis. The aim of the study was to identify the risk factors for 30-day readmission in ethnic minority patients admitted for cirrhosis. Methods: We did a retrospective review of 1,373 electronic medical records of patients with cirrhosis admitted from 2009 to 2011. Several parameters including alcohol use history, discharge location and cirrhosis severity scores - model for end-stage liver disease (MELD) score and Child-Pugh-Turcotte (CPT) at first admission were assessed. Statistical analysis was done using Chi-square test and t -test for categorical and continuous variables, respectively. Results: There were 79 patients in the readmission group (63% male, 54% Hispanics and 22% African Americans) and 104 in the no readmission group (62% male, 58% Hispanics and 24% African Americans). History of alcohol use within a month prior to admission (55% vs. 33%, P = 0.002), platelet count at discharge (89,000 vs. 124,000, P = 0.003), and discharge with more than seven medications per day (7.3 vs. 5.2, P = 0.005) were identified as risk factors for readmissions by multivariate analysis. Conclusion: Platelet count, active alcohol use and more than seven medications at discharge are predictors of readmission. These parameters can guide future interventions to reduce readmission rate and health care costs related to cirrhosis readmissions. Gastroenterol Res. 2020;13(1):11-18 doi: https://doi.org/10.14740/gr1227
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".