First Canadian experience with robotic laparoendoscopic single-site vs. standard laparoscopic living-donor nephrectomy: A prospective comparative study
Bibliographic record
Abstract
INTRODUCTION: We aimed to compare the outcomes of robotic laparoendoscopic single-site living donor nephrectomy (R-LESS LDN) vs. standard laparoscopic living donor nephrectomy (LLDN). METHODS: Between October 2013 and November 2015, 39 patients were allocated to either standard LLDN (n=25) or R-LESS LDN (n=14). Patient demographics, perioperative outcomes, analgesic requirement, visual analogue scale of pain at postoperative days 1, 3, 7, and 30, and a health-related quality of life and body image questionnaire were prospectively collected. RESULTS: There were no significant differences in demographics and intraoperative outcomes between the two cohorts. The R-LESS LDN cohort had lower analgesic requirement (p=0.002) and lower visual pain scores on days 1 and 3 (p=0.001). Additionally, body image and satisfaction scores in the R-LESS group were also superior compared to the LLDN cohort (p=0.008). There was no significant difference in the postoperative complications according to the Clavien-Dindo system. Recipient graft functional outcomes were equivalent. CONCLUSIONS: This is the first evidence that R-LESS LDN is safe and associated with comparable surgical and early functional outcomes compared to LLDN, while pain, donor body image, and satisfaction scores were improved compared to LLDN.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".