Bibliographic record
Abstract
Introduction: Given the number of patients with end-stage renal disease on the kidney transplant wait-list, transplant programs across Canada are developing strategies to increase the available donor pool.One potential strategy is to limit post-nephrectomy morbidity for living kidney donors through the use of robotic single incision laparoscopic surgery.Methods: We performed a prospective single-centre study comparing robotic single incision laparoscopic nephrectomy (n=5) with standard multiple incision laparoscopic nephrectomy (n=7) in living kidney donors.We compared operating time, length of hospital stay, and self-reported pain scale.Results: Demographics were similar between the robotic and standard laparoscopic donor groups in regards to age (mean 51 years) and body mass index (BMI).Operative time was also comparable between the two groups at 268 minutes and 255 minutes, respectively.The robotic group length of hospital stay was slightly longer at 3.4 days compared to 3.0 days for the standard group.Postoperative pain scores on day 1 and 7 were 4.8 and 2/10 in the robotic group versus 5.5 and 2.4/10 in the standard laparoscopic group.There were no significant intraoperative or postoperative complications in either group.Conclusions: Our early experience with single incision robotic donor nephrectomy suggests that this procedure is safe and comparable to standard laparoscopic donor nephrectomy.With an increasing sample size and longer postoperative follow-up period, we hope to determine whether decreased postoperative pain scores following robotic laparoscopic surgery results in shorter length of hospital stay and faster recovery time for living kidney donors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.541 | 0.224 |
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".