Bilateral En-Block Horseshoe Kidney Laparoscopic Nephrectomy
Bibliographic record
Abstract
Abstract Horseshoe kidney (HSK) has a prevalence of 1 in every 500 individuals. The management of patients with HSK is usually conservative, except in the presence of symptoms such as obstruction, stones, glomerulopathies, and tumors. In the following case report, we describe how a bilateral en-block transmesenteric laparoscopic nephrectomy in supine position was performed. A 5-year-old boy, with proximal hypospadias and early onset of chronic kidney disease due to focal segmental glomerulosclerosis on biopsy, underwent a genetic molecular evaluation that confirmed a pathogenic mutation at the WT-1 gene. Due to the increased risk of developing Wilms tumor, he underwent a bilateral transmesenteric nephrectomy. In a five-minute video, we describe how we performed an en-block transperitoneal and transmesenteric laparoscopic nephrectomy with special attention to patient positioning, including the feasibility of performing the dissection of the left renal hilum and isthmus with the patient in supine with no need for repositioning, and then moving to the dissection of the right renal hilum and completion of the procedure. The case herein reported enables us to describe the technical key-points to perform a bilateral en-block laparoscopic nephrectomy with shorter operative time and reduction of blood loss by preserving the entire specimen, without the need for an isthmus transection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".