Assessing the Predictive Efficacy of Lok Score in Identifying Esophageal Varices in Liver Cirrhosis Patients: A Cross-Sectional Study
Bibliographic record
Abstract
Introduction Liver cirrhosis is a global health concern with various etiologies, leading to portal hypertension and gastroesophageal varices. Variceal bleeding, a severe complication of cirrhosis, necessitates early detection and intervention to reduce mortality. Endoscopic screening is the gold standard for varices detection but is invasive and expensive. This study evaluates the Lok Score, a non-invasive predictive tool, for identifying esophageal varices in patients with liver cirrhosis. Materials and methods A cross-sectional study involving 150 liver cirrhosis patients was conducted. The Lok score was calculated using specific parameters. Patient data, including age, gender, etiology of liver cirrhosis, Child-Pugh class, varices presence, and grades, were recorded. Statistical analysis was performed using IBM Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp., and diagnostic parameters for Lok Score were computed. Results The study demonstrates that the Lok score exhibits significant potential as a predictive tool for esophageal varices. The mean Lok score significantly differed between individuals with and without varices, suggesting a correlation between Lok score and varices presence. Higher Lok scores may indicate more advanced varices. Utilizing the Lok score in clinical practice could lead to timely interventions, improving patient outcomes. Conclusion The Lok score shows promise as a valuable predictive tool for esophageal varices in liver cirrhosis patients. Early identification using this non-invasive parameter can aid in risk stratification and guide appropriate management strategies. However, further validation and larger studies are needed to fully integrate the Lok score into clinical practice for the benefit of cirrhosis patients.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".