The need for histological subclassification of cirrhosis: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND & AIMS: The need for further histological subclassification of cirrhosis has been increasingly recognized because of the heterogeneity of severity within cirrhosis. We sought to identify evidence in the literature regarding the histological subclassification of cirrhosis using the Laennec stage. METHODS: We conducted a systematic review and meta-analysis by searching databases, including MEDLINE, EMBASE and the COCHRANE library, for relevant studies. RESULTS: Of 208 studies identified, 16 were eligible according to the inclusion criteria. With higher grades of the Laennec stage, clinical stages of cirrhosis and Child-Pugh scores/Model for end-stage liver disease scores increased (P < 0.05). Higher Laennec stages were statistically associated with the development of liver-related events, such as liver-related death, liver cancer progression and variceal haemorrhage, as well as higher hepatic venous pressure gradients and higher liver stiffness values (P < 0.05). Two open-labelled studies showed the usefulness of the Laennec system with regard to the evaluation of whether antifibrotic treatments were effective. The mean kappa value was 0.81 (range 0.61-0.87) for inter-observer agreement. CONCLUSIONS: Based on this systematic review and meta-analysis, histological subclassification of cirrhosis using the Laennec system is useful to better predict prognosis and complications of portal hypertension.
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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 | 0.023 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".