Minimally invasive nephrectomy for inflammatory renal disease
Bibliographic record
Abstract
OBJECTIVE: Once chronic inflammatory renal disease (IRD) develops, it creates a severe peri-fibrotic process, which makes it a relative contraindication for minimally invasive surgery (MIS). Our objective is to show that laparoscopic nephrectomy (LN) is a surgical option in IRD with fewer complications and better outcomes. METHODS: Retrospective review of patients who underwent a modified-surgical laparoscopic transperitoneal nephrectomy was performed. Data search included all operated patients between May 2013 and May 2018 that had a pathology result with any renal inflammatory condition (xanthogranulomatous pyelonephritis, chronic nephritis, and renal tuberculosis). We describe intra-operative variables such as operative time, blood loss, conversion rate, postoperative complications and length of hospital stay. RESULTS: There were 51 patients who underwent laparoscopic nephrectomy with a confirmatory pathology report for IRD. We identified four (8%) major complications; three of them required transfusion and one conversion to open surgery. The mean operative time was 233±108 min. Mean estimated blood loss was 206±242 mL excluding the conversion cases and 281±423 mL including them. The mean length of hospital stay was 3.0±2.0 days. CONCLUSION: Laparoscopic nephrectomy for IRD can safely be done. It is a reproducible technique with low risks and complication rates. Our experience supports that releasing the kidney first and leaving the hilum for the end is a safe approach when vascular structures are embedded into a single block of inflammatory and scar tissue.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".