Optimizing associated liver partition and portal vein ligation for staged hepatectomy outcomes: Surgical experience or appropriate patient selection?
Bibliographic record
Abstract
Background: Early reports of associated liver partition and portal vein ligation for staged hepatectomy (ALPPS) outcomes have been suboptimal. The literature has confirmed that learning curves influence surgical outcomes. We have 54 months of continuous experience performing ALPPS with strict selection criteria. This study aimed to evaluate the impact of the learning curve on ALPPS outcomes. Methods: We retrospectively compared patients who underwent ALPPS between April 2012 and March 2016. Patients were grouped into 2 24-month (early and late) periods. All candidates had a high tumour load requiring staged hepatectomy after chemotherapy response, a predicted future liver remnant (FLR) less than 30% and good performance status. Results: Thirty-three patients underwent ALPPS during the study period: 16 in the early group (median age 65 yr, mean body mass index [BMI] 27) and 17 in the late group (median age 60 yr, mean BMI 25). Bilobar disease was comparable in both groups (94% v. 88%, p > 0.99). Duration of surgery was not statistically different. Intraoperative blood loss and need for transfusion were significantly lower in the late group (200 ± 109 mL v. 100 ± 43 mL, p < 0.05). The late group had a higher proportion of monosegment ALPPS (4:1). There were no deaths within 90 days in either cohort. Rates of postoperative complications were not statistically significant between groups. The R0 resection rate was similar. The entire 1-year disease-free and overall survival were 52% and 84%, respectively. Conclusion: Excellent results can be obtained in innovative complex surgery with careful patient selection and good technical skills. Additionally, the learning curve brought confidence to perform more complex procedures while maintaining good outcomes.
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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 | 0.008 |
| 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 | 0.001 | 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".