Retroperitoneoscopic Standard or Hand-Assisted Versus Laparoscopic Standard or Hand-Assisted Donor Nephrectomy: A Systematic Review and the First Network Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: At the present four minimally invasive procedures namely retroperitoneoscopic (RPDN), laparoscopic (LPDN), hand-assisted retroperitoneoscopic (HARDN) and hand-assisted laparoscopic donor nephrectomy (HALDN) are used to perform donor nephrectomies. The current evidence based on retrospective studies and on pairwise only meta-analyses is inconclusive. Up to authors' best knowledge there is no so far network meta-analysis to compare all the above-mentioned procedures. Therefore, a network meta-analysis was conducted to compare the feasibility, safety and reproducibility of the four donor nephrectomies procedures. METHODS: Google Scholar, EMBASE, PubMed, and Cochrane library were used for a systematic literature search. Both updated pairwise and network meta-analyses were performed. RESULTS: Compared to LPDN there was evidence of significantly more right kidneys retrieved with RPDN; nonsignificant differences demonstrated both with HALDN and HARDN compared to LPDN. There was evidence that the operative time was significantly shorter by 77 min in RPDN compared to LPDN; on the other hand, HARDN and HALDN did not demonstrate significant differences when compared to LPDN. CONCLUSIONS: The present study demonstrates that each approach can be applied safely in adequately selected patients. Moreover, retroperitoneoscopic is reliable, safe and easily reproducible alternative of LPDN for both left and right kidneys.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".