Liver Histology Predicts Liver Regeneration and Outcome in ALPPS
Bibliographic record
Abstract
BACKGROUND AND AIMS: Alterations in liver histology influence the liver's capacity to regenerate, but the relevance of each of the different changes in rapid liver growth induction is unknown. This study aimed to analyze the influence of the degree of histological alterations during the first and second stages on the ability of the liver to regenerate. METHODS: This cohort study included data obtained from the International ALPPS Registry between November 2011 and October 2020. Only patients with colorectal liver metastases were included in the study. We developed a histological risk score based on histological changes (stages 1 and 2) and a tumor pathology score based on the histological factors associated with poor tumor prognosis. RESULTS: In total, 395 patients were included. The time to reach stage 2 was shorter in patients with a low histological risk stage 1 (13 vs 17 days, P ˂0.01), low histological risk stage 2 (13 vs 15 days, P <0.01), and low pathological tumor risk (13 vs 15 days, P <0.01). Regarding interval stage, there was a higher inverse correlation in high histological risk stage 1 group compared to low histological risk 1 group in relation with future liver remnant body weight ( r =-0.1 and r =-0.08, respectively), and future liver remnant ( r =-0.15 and r =-0.06, respectively). CONCLUSIONS: ALPPS is associated with increased histological alterations in the liver parenchyma. It seems that the more histological alterations present and the higher the number of poor prognostic factors in the tumor histology, the longer the time to reach the second stage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".