Experience with sliding-clip splenorrhaphy for splenic injury during radical nephrectomy
Bibliographic record
Abstract
INTRODUCTION: We report our experience with sliding-clip splenorrhaphy (SCS), a novel splenic conservation technique, for iatrogenic splenic injury (ISI) during a left radical nephrectomy (RN). We also reviewed the literature on ISI. METHODS: We retrospectively reviewed data from patients who had RN between January 2005 and December 2013 at our institution. The technique used was similar to sliding-clip renorrhaphy. Our Medline literature identified articles containing "splenic injury during nephrectomy," "iatrogenic splenic injury." "iatrogenic splenectomy," and "splenorrhaphy." Our primary outcome measures included incidence of splenic injury and splenic conservation rate and splenectomy. RESULTS: Among the 370 RN, 140 were left sided. ISI injury occurred in 6 left RN (incidence 1.6% for all nephrectomies, 4.2% for left-sided nephrectomies). All 6 cases had open procedure and for the left-sided procedure. Splenic conservation was attempted in 4 patients using SCS and 3 out of 4 were successfully repaired. Altogether 3 patients had splenectomy (incidence 0.8% for all nephrectomies, 2.1% for left-sided nephrectomies). Our literature review revealed that the incidence of iatrogenic splenectomy during left nephrectomy varies from 1.3% to 13.2%. CONCLUSIONS: SCS is an acceptable novel splenic conservation technique due to its ease of use and success in selected patients with ISI. There is little evidence on the true incidence of ISI especially on splenic conservation during nephrectomy. Every attempt at splenic conservation is likely to improve immediate- and long-term patient outcomes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".