Impact of Sodium, Potassium, and Calcium on the Child-Pugh and MELD Scores in Assessing the Severity of Liver Cirrhosis: A Two-Year Cross-Sectional Study
Bibliographic record
Abstract
Background and aim Patients with chronic liver disease are prone to experiencing electrolyte imbalances as a result of physiological changes caused by cirrhosis. These imbalances have a detrimental effect on prognosis, morbidity, and mortality. This study aimed to assess the serum concentrations of sodium, calcium, and potassium in patients with liver cirrhosis and determine their correlation with disease severity and prognosis. Methods A cross-sectional study was conducted on 110 patients with liver cirrhosis at the Department of Medicine, Jawaharlal Nehru Medical College (JNMC), Datta Meghe Institute of Higher Education and Research (DMIHER) (DU), Wardha, Maharashtra, India, between December 2020 and November 2022. All patients diagnosed with liver cirrhosis, aged 18 years or older, were categorized into three groups: Child-Pugh class A (n = 5), class B (n = 39), and class C (n = 66). Results Our investigation found a notable significant disparity in serum sodium levels across groups A, B, and C, with the Child-Pugh class A group exhibiting the highest median serum sodium levels. The serum sodium < 137 mg/dL group had the highest median model for end-stage liver disease (MELD) score, and there was a statistically significant difference in the MELD score among the three groups. The distribution of serum potassium levels and results exhibited substantial variation across the groups. Conclusion The integration of sodium, potassium, and calcium levels into predictive models is imperative for accurately forecasting in-hospital mortality among patients with cirrhosis. These electrolytes play vital roles in physiological processes, and their inclusion enhances the predictive power of models, providing clinicians with more precise risk assessments. By incorporating these key variables, healthcare professionals can better tailor interventions and optimize patient care strategies, ultimately improving the outcomes for individuals with cirrhosis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".