Correlation of Surgical Times with Laparoscopic Live Donor Kidney Transplant Outcomes
Bibliographic record
Abstract
Most studies revealed that ischemic time has substantial role in occurrence of delayed graft function (DGF) after deceased donor kidney transplantation. However, less is known about the potential impact of surgical times on early outcomes following live donor kidney transplantation. A retrospective cohort of 189 consecutive laparoscopic live donor kidney transplant (LDKT) recipients from January 2006 to August 2012 was analyzed to reveal the impact of pneumoperitoneum time (PT) and anastomosis time (AT) on donor and recipient length of hospital stay and early graft function (EGF). DGF was observed in 13 (6.8%) patients while slow graft function (SGF) was seen in 27 (14%) of the recipients. The median AT was 28 minutes (interquartile range 23, 35 minutes). AT was associated with DGF (Odds Ratio [OR] 1.044, per minute, 95% CI 1.007, 1.082, p = 0.018). Median recipient length of hospital stay was 8 (interquartile range 7, 11) days. Every 13.5 minutes of longer AT was associated with 1 extra day in hospital. The median PT was 180 minutes (interquartile range 144, 234 minutes). PT was associated with both DGF (OR 1.013 per minute, 95% CI 1.005, 1.021, p = 0.001) and SGF (OR 1.009 per minute, 95% CI 1.002, 1.016, p = 0.016). Every extra hour of PT was associated with 0.42 more days in hospital for the donor. Surgical times may be underestimated variables in dictating use of hospital resources. The effect of surgical times on long term hard outcomes entails further study.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".