"First, do no harm": monitoring outcomes during the transition from open to laparoscopic live donor nephrectomy in a Canadian centre.
Bibliographic record
Abstract
OBJECTIVE: During the learning curve for laparoscopic live donor nephrectomy (LLDN), donor morbidity and poorer graft function may be increased. To minimize these risks, a dedicated team of laparoscopic, urologic and transplant specialists worked together to introduce the technique. This study was undertaken to validate this approach by comparing donor and recipient outcomes and studying our learning curve during the transition from open (OLDN) to LLDN. METHODS: We compared 59 LLDNs with 34 OLDNs performed for adult recipients. Data were collected prospectively for LLDN and retrospectively for OLDN. We compared donor outcomes and recipient graft function in the 2 groups, and we used the cumulative sum (CUSUM) method to generate learning curves; p < 0.05 was considered statistically significant. RESULTS: From the donor standpoint, the complication rate was 10% in the laparoscopic group, compared with 21% in the open group. Length of stay was shorter after LLDN (3 v. 5 d, p < 0.001). Among the recipients, there were no significant differences in the incidences of ureteral complications, delayed graft function (DGF), creatinine levels, acute rejection or patient and graft survival. When we used the incidence of DGF after OLDN as a benchmark, CUSUM analysis revealed a downward inflection point for DGF after 30 cases, consistent with an improvement in performance. CONCLUSION: At our institution, a team approach has allowed the safe introduction of LLDN without a significant negative impact on recipient outcomes and with a reduction in donor length of stay. Using DGF as an outcome, we observed improved performance after 30 cases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".