Early Survival and Safety of ALPPS
Bibliographic record
Abstract
OBJECTIVES: To assess safety and outcomes of the novel 2-stage hepatectomy, Associating Liver Partition and Portal Vein Ligation for Staged Hepatectomy (ALPPS), using an international registry. BACKGROUND: ALPPS induces accelerated growth of small future liver remnants (FLR) to allow curative resection of liver tumors. There is concern about safety based on reports of higher morbidity and mortality. METHODS: A Web-based data entry system was created with password access and data pseudoencryption (NCT01924741). All patients with complete 90-day data were included. Multivariate logistic regression analysis was performed to identify independent risk factors for severe complications and mortality and volume growth of the FLR. RESULTS: Complete data were available for 202 patients. A total of 141 (70%) patients had colorectal liver metastases (CRLM). Median starting standardized future liver remnants of 21% increased by 80% within a median of 7 days. Ninety-day mortality was 19/202 (9%). Severe complications including mortalities (Clavien-Dindo≥IIIb) occurred in 27% of patients. Independent factors for severe complications were red blood cell transfusion [odds ratio (OR), 5.2), ALPPS stage I operating time greater than 300 minutes (OR, 4.4), age more than 60 years (OR, 3.8), and non-CRLM (OR, 2.7). Age, use of Pringle maneuver, and histologic changes led to less volume growth. In patients younger than 60 years with CRLM, 90-day mortality was similar to conventional 2-stage hepatectomies for CRLM. CONCLUSIONS: This is the first analysis of the ALPPS registry showing that ALPPS has increased perioperative morbidity and mortality in older patients but better outcomes in patients with CRLM.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".