Noninvasive prediction of large esophageal varices in liver cirrhosis patients
Bibliographic record
Abstract
PURPOSE: Esophageal varices are a dangerous complication of liver cirrhosis. The development of cost effective, noninvasive means for prediction of large esophageal varices could reduce the use of upper gastrointestinal endoscopy in variceal screening and also provide an alternative way to confirm the results of conventional endoscopic diagnosis. Previously proposed predictive models are neither sensitive nor specific. METHODS: A retrospective study based on a group of 104 liver cirrhosis patients was performed. Multiple statistical approaches were used to evaluate the association of large esophageal varices with 20 individual and six compound clinical laboratory variables. A new predictive model was developed. RESULTS: Univariate analysis suggested that eight out of 26 variables were significantly associated with large esophageal varices. Further stepwise logistic regression eventually identified three variables (hemoglobin level, portal vein diameter and the ratio of platelet count/spleen diameter) that contributed significantly to the final regression model. Receiver operating characteristic (ROC) curve analysis showed that this new regression model achieved 77.8% and 72% of diagnostic sensitivity and specificity, respectively, for the prediction of large esophageal varices. In our study group, its diagnostic accuracy (AUROC=0.814) was found to be significantly higher than six predictive models previously published. CONCLUSIONS: No single variable offers self-sufficient predictive function for large esophageal varices. A comprehensive model using multiple variables significantly improves the predictive accuracy in screening the most at risk patients with potential variceal hemorrhage.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".