Correlation between Platelet Count and Grading of Esophageal Varices in Liver Cirrhosis Patients: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Esophageal varices are a major complication of liver cirrhosis. Esophageal varices bleeding is life-threatening and an urgent medical emergency. Low platelet count and esophageal varices are common findings in liver cirrhosis. Platelet count is suggested as a non-invasive screening tool to predict the grading of esophageal varices in liver cirrhosis patients. Several studies have found a correlation between platelet count and grading of esophageal varices in liver cirrhosis patients. However, the results are conflicting. AIM: This meta-analysis aimed to evaluate the correlation between platelet count and the grading of esophageal varices in liver cirrhosis patients. METHODS: A systematic literature search was performed through the database search from PubMed, SCOPUS, Ovid EMBASE, and EuropePMC to obtain all relevant articles with the following search terms: "correlation" and "platelet" or "thrombocytopenia" AND "esophageal varices" and "liver cirrhosis" or "chronic liver disease" that were published within the year of 2000-2021. Articles were collected by using PRISMA flow diagrams. The data were extracted from the eligible study within inclusion and exclusion criteria. The quality of each study was assessed using the Newcastle Ottawa Scale (NOS). A meta-analysis was conducted to determine the overall pooled correlation coefficient (r) and 95% confidence interval (CI). RESULTS: There were a total of 1008 patients from eight included studies. The meta-analysis showed that the pooled correlation coefficient between platelet count and grading of esophageal varices in liver cirrhosis patients was r = -0.42 (95%CI -0.65 to -0.13; p = 0.005; I2 = 96.06%). CONCLUSION: There was a moderate negative correlation between platelet count and grading of esophageal varices. Thus, low platelet count may indicate higher grades of esophageal varices in liver 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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.043 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".