Laparoscopic nephrectomy for massive polycystic kidney disease: Updated technique and outcomes
Bibliographic record
Abstract
INTRODUCTION: We present our technique of laparoscopic nephrectomy for massive polycystic kidneys in patients with autosomal dominant polycystic kidney disease (ADPKD) and review the outcome analysis of our experience. METHODS: We retrospectively reviewed all transperitoneal laparoscopic nephrectomies done for polycystic kidneys at a university hospital. Our technique included three 12-mm ports with additional one or two 5-mm ports, with usage of retraction devices, such as the Jarit PEER retractor (J. Jamner Surgical Instruments, Inc, Hawthorne, NY). RESULTS: In total, 39 (left 14, right 25) laparoscopic nephrectomies were performed in 32 patients (male 21, female 11). Surgical indications were varied: to create space for future renal transplant in 21 (54%), to alleviate pain in 16 (41%), to prevent recurrent urosepsis in 2 (5%), to prevent recurrent bleeding which would require transfusions in 2 (5%) and to remove a renal tumour in 1 kidney (2.5%). Four patients had surgery for more than one reason. The mean age and body mass index were 52.2 years (range: 29-72) and 26.9 kg/m(2) (range: 21.6-34.0), respectively. The mean preoperative hemoglobin and serum creatinine levels were 131.6 g/L (range: 107-171) and 514 µmol/L (range: 84-923), respectively; 26 (81%) patients were on dialysis. The mean operative time and estimated blood loss were 185 minutes (range: 113-287) and 94 mL (range: 10-350), respectively. No patient required open conversion. The mean specimen size was 24.2 cm (range: 15-38); weight 1515 g (range: 412-4590) and the length of extraction incision was 9.2 cm (range: 6-13). There were 1 Grade 2 2 (2.5%), 2 Grade 3b (5%) and 1 Grade 4a-d (2.5%) complications. The mean length of stay was 4.5 days (range: 3-8). CONCLUSIONS: Our technique of laparoscopic nephrectomy for massively enlarged polycystic kidneys in ADPKD is safe and offers all the advantages of minimal access surgery, such as smaller incision, decreased estimated blood loss, excellent cosmesis and faster recovery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".