MELD score and antibiotics use are predictors of length of stay in patients hospitalized with hepatic encephalopathy
Bibliographic record
Abstract
BACKGROUND: Hepatic encephalopathy (HE) represents a significant burden to the healthcare system. The aim of this study was to determine factors influencing the hospital length of stay among patients hospitalized with HE. METHODS: A data warehouse query was performed to identify 316 patients with a first hospitalization during which HE occurred, between April 2010 and February 2012. Baseline and hospitalization characteristics were collected with IRB approval. A negative binomial multivariable model was used to control for potential confounders on the length of hospitalization. RESULTS: Median age was 59 years, and 60.4% of admitted patients were male. The median MELD score was 22 (IQR: 17-28). Median length of stay was 8 days (IQR: 3.25-14.25). After controlling for MELD score, female gender (2.2 days; p = 0.04), being initially admitted for a reason other than HE (liver-related: 7.6 days; p < 0.01 and non liver-related 10.7 days; p < 0.01) and receiving antibiotics other than rifaximin (10.5 days; p < 0.01) were associated with longer length of stay whereas hepatitis C (-3.1 days; p < 0.01) was associated with a shorter length of stay. CONCLUSIONS: MELD score, gender, use of antibiotics other than rifaximin, reason for admission and hepatitis C are predictors readily available in clinic that can help identify patients at risk for longer length of stay.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".