Systematic review and meta-analysis of open versus laparoscopy-assisted versus pure laparoscopic versus robotic living donor hepatectomy
Bibliographic record
Abstract
The value of minimally invasive approaches for living donor hepatectomy remains unclear. Our aim was to compare the donor outcomes after open versus laparoscopy-assisted versus pure laparoscopic versus robotic living donor hepatectomy (OLDH vs. LALDH vs. PLLDH vs. RLDH). A systematic literature review of the MEDLINE, Cochrane Library, Embase, and Scopus databases was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement (up to December 8, 2021). Random-effects meta-analyses were performed separately for minor and major living donor hepatectomy. The risk of bias in nonrandomized studies was assessed using the Newcastle-Ottawa Scale. A total of 31 studies were included. There was no difference in donor outcomes after OLDH versus LALDH for major hepatectomy. However, PLLDH was associated with decreased estimated blood loss, length of stay (LOS), and overall complications versus OLDH for minor and major hepatectomy, but also with increased operative time for major hepatectomy. PLLDH was associated with decreased LOS versus LALDH for major hepatectomy. RLDH was associated with decreased LOS but with increased operative time versus OLDH for major hepatectomy. The scarcity of studies comparing RLDH versus LALDH/PLLDH did not allow us to meta-analyze donor outcomes for that comparison. There seems to be a marginal benefit in estimated blood loss and/or LOS in favor of PLLDH and RLDH. The complexity of these procedures limits them to transplant centers with high volume and experience. Future studies should investigate self-reported donor experience and the associated economic costs of these approaches.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.044 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".