The optimal cut‐off for predicting large oesophageal varices using transient elastography is disease specific
Bibliographic record
Abstract
The diagnosis of cirrhosis requires screening for oesophageal varices by upper gastrointestinal endoscopy. In many countries, serological tests and elastography are replacing liver biopsy for diagnosing cirrhosis. The aims of this study were to see whether there was an optimal cut-off of liver stiffness that could predict the presence of large (>F2) oesophageal varices and whether this was disease specific. A total of two hundred and twenty-two patients with all cause cirrhosis (Child class A) were screened, and 211 had successful elastography and are included in the analysis. Of the patients studied, one hundred and thirty-two patients had no or small F1 varices and 79 had large varices. Liver stiffness of 19.8 kPa had a negative predictive value of 91% and a positive predictive value of 55% with an area under the curve (AUC) on receiver operating characteristics (ROC) of 0.73 in differentiating between small and large varices. Seven patients with large varices would have been incorrectly classified. In the 157 patients with hepatitis C as the aetiology of cirrhosis, the negative predictive value was 98% and only one patient was misclassified. Liver stiffness was superior in diagnostic accuracy to platelet count in all patients. A liver stiffness of >19.8 kPa could be utilized as a cut-off for endoscopy and beta blocker utilization, particularly in patients with hepatitis C.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".