Laparoscopic partial nephrectomy for renal cell carcinoma in a horseshoe kidney
Bibliographic record
Abstract
Horseshoe kidney has an incidence rate ranging from 1 in 400 to 1 in 1000, with a 2:1 ratio in men. It also has a predilection for chromosomal aneuploidies. From a pathophysiology standpoint, this anomaly occurs during the second to sixth week of gestation when the inferior portion of the metanephric blastema fuses to form an isthmus, commonly in the lower renal pole (90%). As a result of this fusion, the kidney may not bypass the inferior mesenteric artery and is impeded in its ascent. With an aberrant anatomical orientation and location, complications arise including hydronephrosis, renal calculi and a twofold risk of Wilms tumour. Despite these findings, the association of renal cell carcinoma (RCC) within a horseshoe kidney is extremely rare and fewer than 200 cases have been described. Therapeutically speaking, partial nephrectomies are the gold standard of treatment for renal tumours smaller than 4 cm in diameter, with a growing indication to accomplish this procedure by laparoscopic or robotic means. We report a case of an asymptomatic 58-year-old male with an incidental computed tomography scan finding of a 4-cm solid mass in the right moiety of a horseshoe kidney. He was treated by laparoscopic partial nephrectomy. There have only been 2 other reported cases to our knowledge on a laparoscopic partial nephrectomy in a horseshoe kidney for RCC. We believe that, in experienced hands, the laparoscopic approach may be used successfully for this clinical situation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".