Prediction of Mortality After ALPPS Stage-1
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to identify predictors of 90-day mortality after Associating Liver Partition and Portal Vein Ligation for Staged Hepatectomy (ALPPS), available after stage-1, either to omit or delay stage-2. BACKGROUND DATA: ALPPS is a two-stage hepatectomy for patients with extensive liver tumors with predicted small liver remnants, which has been criticized for its high mortality rate. Risk factors for mortality are unknown. METHODS: Patients in the International Registry undergoing ALPPS from April 2011 to July 2014 were analyzed. Primary outcome was 90-day mortality. Liver function after stage-1 was assessed using the criteria of the International Study Group for Liver Surgery (ISGLS) after stage-1 among others. A multivariable model was used to identify independent predictors of 90-day mortality. RESULTS: Three hundred twenty patients registered by 55 centers worldwide were evaluated. Overall 90-day mortality was 8.8% (28/320). The predominant cause for 90-day mortality was postoperative liver failure in 75% of patients. Fourteen percent of patients developed liver failure according to ISGLS criteria already after stage-1 ALPPS. Those and patients with a model of end-stage liver disease (MELD) score more than 10 before stage-2 were at significantly higher risk for 90-day mortality after stage-2 with an odds ratio (OR) 3.9 [confidence interval (CI) 1.4-10.9, P = 0.01] and OR 4.9 (CI 1.9-12.7, P = 0.006), respectively. Other factors, such as size of future liver remnant (FLR) before stage-2 and time between stages, were not predictive. CONCLUSIONS: This analysis of the largest cohort of ALPPS patients so far identifies those patients in whom stage-2 ALPPS surgery should be delayed or even denied. These findings may help to make ALPPS safer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".