Transvaginal natural orifice specimen extraction surgery (NOSES) in 3D laparoscopic partial or radical nephrectomy: a preliminary study
Bibliographic record
Abstract
BACKGROUND: With the development of minimally invasive technology, more and more people pay attention to aesthetics of the wound after operation. This study is aim to introduce a new surgical technique of transvaginal natural orifice specimen extraction surgery (NOSES) in 3D laparoscopic partial or radical nephrectomy and evaluate the safety, feasibility and clinical effect. METHODS: Eleven patients who underwent 3D laparoscopic partial nephrectomy (n = 7) or radical nephrectomy (n = 4) and NOSES were included in this study. The surgical procedures and techniques, especially the NOSES operation, are reported in detail. In addition, the basic clinical data, perioperative related data, perioperative complications were analyzed. RESULTS: All 11 patients were performed successfully without conversion to open surgery. The mean total operative time was 133 (84, 150) min. NOSES time was 15 (13, 16) min, and the postoperative hospital stay was 5 (5, 5) d. The mean visual analogue score (VAS) was 3 (2, 4) point and 1 (0, 1) point at 24 h and 48 h after operation, respectively. No patient had recurrence, metastasis and death during the follow-up period of 3 to 17 months. The median Vancouver Scar Scale (VSS) was 1 (1, 1) point. The mean of Female Sexual Function Index (FSFI) was 21.60 (20.20, 21.60), 21.80 (19.80, 21.80) respectively between preoperative and postoperative 3 months, which has no statistical difference (P = 0.179). There was no statistical difference in the Pelvic Floor Distress Inventory-short form 20 (PFDI-20) score between preoperative and postoperative 3 months (P = 0.142). CONCLUSIONS: Transvaginal NOSES is safe and feasible in 3D laparoscopic partial or radical nephrectomy. Furthermore, it results in low incision-related pain without affecting the pelvic floor and sexual function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".