Optimizing left-sided live kidney donation: hand-assisted retroperitoneoscopic as alternative to standard laparoscopic donor nephrectomy
Bibliographic record
Abstract
Laparoscopic donor nephrectomy (LDN) is less traumatic and painful than the open approach, with shorter convalescence time. Hand-assisted retroperitoneoscopic (HARP) donor nephrectomy may have benefits, particularly in left-sided nephrectomy, including shorter operation and warm-ischemia time (WIT) and improved safety. We evaluated outcomes of HARP alongside LDN. From July 2006 to May 2008, 20 left-sided HARP procedures and 40 left-sided LDNs were performed. Intra and postoperative data were prospectively collected and analysis on outcome of both techniques was performed. More female patients underwent HARP compared to LDN (75% vs. 40%, P = 0.017). Other baseline characteristics were not significantly different. Median operation time and WIT were shorter in HARP (180 vs. 225 min, P = 0.002 and 3 vs. 5 min, P = 0.007 respectively). Blood loss did not differ (200 ml vs.150 ml, P = 0.39). Intra and postoperative complication rates for HARP and LDN (respectively 10% vs. 25%, P = 0.17 and 5% vs. 15%, P = 0.25) were not significantly different. During median follow-up of 18 months estimated glomerular filtration rates in donors and recipients and graft- and recipient survival did not differ between groups. Hand-assisted retroperitoneoscopic donor nephrectomy reduces operation and warm ischemia times, and provides at least equal safety. Hand-assisted retroperitoneoscopic may be a valuable alternative for left-sided LDN.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".