Laparoscopy, dorsal lumbotomy and flank incision live donor nephrectomy: comparison of donor outcomes
Bibliographic record
Abstract
BACKGROUND: Flank incision (FL), dorsal lumbotomy (DL) and laparoscopic surgery have been effective approaches to donor nephrectomy. While laparoscopic donor nephrectomy (LDN) has become increasingly popular, there has yet to be a direct comparison of the three modalities. METHODS: We performed a retrospective chart review of FL, DL and LDN operations between 2002 and 2010 within a single institution. Donor and recipient characteristics, as well as surgical outcomes, were assessed. RESULTS: There were 496 donor nephrectomy operations available for analyses. Patients in the LDN group had the lowest estimated blood loss, compared to the DL and FL groups (p < 0.001), lowest rate of complications (p < 0.01), and shortest hospital stay (p < 0.0001). Donors who underwent DL used an average of 60.12 ± 5.0 mg of morphine, which was significantly less than that used by patients in the LDN (93.2 mg, p < 0.0001) and FL (111.82 mg, p < 0.001) groups. Mean serum creatinine of recipients at day 1 post-op was the highest in the FL group (p < 0.0001 FL vs. LDN, p < 0.001 FL vs. DL), but there were no significant differences between the three groups at 2 weeks, 6, 12, 18, and 24 months post-operation (p > 0.45). CONCLUSIONS: Although a lower pain experience of LDN was not indicated, the use of LDN should be favoured over DL and FL as it is associated with fewer complications, and shorter length of stay. Of note, DL appears to be associated with higher complications and is likely not a preferred option for donor nephrectomy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".