Is Laparoscopic Hepatectomy Safe for Giant Liver Tumors? Proposal from a Single Institution for Totally Laparoscopic Hemihepatectomy Using an Anterior Approach for Giant Liver Tumors Larger Than 10 cm in Diameter
Bibliographic record
Abstract
Background: The efficacy and safety of laparoscopic liver resections for liver tumors that are larger than 10 cm remain unclear. We developed a safe laparoscopic right hemihepatectomy for giant liver tumors using an anterior approach. Methods: Eighty patients who underwent laparoscopic hemihepatectomy between January 2011 and December 2021 were divided into a nongiant tumor group (n = 65) and a giant tumor group (n = 15) for comparison. Results: The median operating time, amount of blood loss, and length of postoperative hospital stay did not differ significantly between the nongiant and giant tumor groups. The sizes of the tumors and weights of the resected liver were significantly larger in the giant tumor group. A comparison between a nongiant group (n = 23) and a giant group (n = 12) treated with laparoscopic right hemihepatectomy showed similar results. Conclusions: Laparoscopic hemihepatectomy, especially that performed on the right side, for giant tumors larger than 10 cm can be performed safely. Surgical techniques for giant liver tumors have been standardized, and their application is expected to spread widely in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".