Rotterdam score predicts early mortality in Budd‐Chiari syndrome, and surgical shunting prolongs transplant‐free survival
Bibliographic record
Abstract
BACKGROUND: Budd-Chiari syndrome carries significant mortality, but factors predicting this outcome are uncertain. AIM: To determine factors associated with 3-month mortality and compare outcomes after surgical shunting or liver transplantation. METHODS: From 1985 to 2008, 51 patients with Budd-Chiari syndrome were identified. RESULTS: By logistic regression analysis, features associated with higher risk of 3-month mortality were Rotterdam class III, Clichy >6.6, model for end-stage liver disease (MELD) >20 and Child-Pugh C. Rotterdam class III had the best performance to discriminate 3-month mortality with sensitivity of 0.89 and specificity of 0.63, whereas Clichy >6.60 had sensitivity of 0.78 and specificity of 0.69; MELD >20 had sensitivity of 0.78 and specificity of 0.75 and Child-Pugh C had sensitivity of 0.67 and specificity of 0.72. Eighteen patients underwent surgical shunts and 14 received liver transplantation with no significant differences in survival (median survival 10 +/- 3 vs. 8 +/- 2 years; log-rank, P = 0.9). CONCLUSIONS: Rotterdam score is the best discrimination index for 3-month mortality in Budd-Chiari syndrome and should be used preferentially to determine treatment urgency. Surgical shunts constitute an important therapeutic modality that may help save liver grafts and prolong transplantation-free survival in a selected group of patients with Budd-Chiari syndrome.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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".