Predictors of 30-day readmission and hospitalization costs of patients with hepatic encephalopathy in the US from 2010 to 2014
Bibliographic record
Abstract
Hepatic encephalopathy (HE) is a complex and reversible neuropsychiatric syndrome that is associated with growing, substantial healthcare resource utilization. We aim to examine the predictors of 30-day readmission and hospitalization cost associated with HE. We conducted a cross-sectional study using the Nationwide Readmissions Database from 2010 to 2014. We assessed the readmission rates using multivariate logistic regression and established temporal trends of readmission rates and hospitalization cost. Weighted hierarchical logistic regression and generalized linear mixed models were used to identify predictors for nationally representative readmissions and hospitalization costs, respectively. The number of index hospitalizations with HE increased with a significant trend from 34,967 in 2010 to 44,791 in 2014. 16.8% of patients were readmitted within 30 days. Predictors increasing readmission risk included female sex, Elixhauser readmission score < 25, elective admission, patient’s state residential status, privately insured, number of diagnoses >13, and length of stay >4 days. Our results indicate there is a need to implement better management strategies to improve outcomes in patients hospitalized with HE to curb the increase in the economic burden associated with the disease.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".