Protein–calorie malnutrition as a prognostic indicator of mortality among patients hospitalized with cirrhosis and portal hypertension
Bibliographic record
Abstract
BACKGROUND: We conducted a nationwide analysis of the prevalence of protein-calorie malnutrition (PCM) in patients with cirrhosis and portal hypertension (PHTN) and to determine its mortality and economic impact. METHODS: We used the Nationwide Inpatient Sample (NIS) to identify admissions throughout the US with cirrhosis and PHTN between 1998 and 2005 using the International Classification of Diseases, 9th Revision diagnostic codes. Prevalence of PCM in this group of patients with cirrhosis was compared with that of general medical inpatients. The impact of PCM on in-hospital mortality was quantified using multiple logistic regression analysis. RESULTS: There were 114 703 admissions with cirrhosis and PHTN in the NIS between 1998 and 2005. The prevalence of PCM was substantially higher among patients with cirrhosis and PHTN compared with general medical inpatients (6.1 vs. 1.9%, P<0.0001), with an adjusted odds ratio of 1.55 (95% CI: 1.4-1.7). There was greater prevalence of ascites (64.6 vs. 47.8%, P<0.0001) and hepatorenal syndrome (5.1 vs. 2.8%, P<0.0001) among those with PCM and cirrhosis. In-hospital mortality was two-fold higher among patients with cirrhosis and PCM (14.1 vs. 7.5%, P<0.0001), with an adjusted mortality of 1.76 (95% CI: 1.59-1.94). PCM was associated with greater length of stay (8.7 vs. 5.7 days, P<0.0001) and hospital charges (US$36 818 vs. US$22 673; P<0.0001) among patients with cirrhosis. CONCLUSIONS: PCM is more common among patients with cirrhosis and PHTN than the general medical population, and is associated with higher in-hospital mortality and resource utilization. PCM may be an indicator of greater disease severity and should be routinely assessed on admission.
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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 | 0.002 |
| 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 | 0.000 | 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".