Optimizing Growth of the Future Liver Remnant and Making In-Situ Liver Transsection Safe—A Standardized Approach to ISLT or ALPPS
Bibliographic record
Abstract
In-situ splitting of the liver before extended resection has gained broad attention. This two-step procedure requires several measures to make an effective and safe procedure. Although the procedure is performed in many institutions, there is no consensus on a uniform technique. The two steps can be divided into different parts and a standardized technique may render the procedure safer and the results will be easier to evaluate. In this paper, we describe a detailed approach to in-situ splitting that allows making both procedures safe, avoids liver necrosis, and is easily reproducible. In the first procedure the portal branches to segments I and IV to VIII are divided, the arterial branches and bile ducts to these segments are preserved and encircled and the parenchyma between segments II/III and IVa/b is divided. This avoids necrosis and bile leaks of segments I and IV and avoids urgent completion operations. In particular, the handling of vital structures close to the dissection line seems important to us. Complete splitting and securing the right and middle hepatic vein will make the second step of this procedure a minimal-risk procedure at a stage where the patient is still recovering from the more demanding first step.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".