Trans-uretero-cystic external urethral stent for urinary diversion in pediatric laparoscopic pyeloplasty
Bibliographic record
Abstract
PURPOSE: We present a new approach for urine drainage in pediatric patients following laparoscopic pyeloplasty, the trans-uretero-cystic external urethral stent (TEUS). METHODS: We retrospectively identified 85 children who underwent laparoscopic pyeloplasty from July 2015 to June 2017. The included children were assigned to group A (double-J stent) or group B (TEUS). In group A, the double-J stent was removed by a cystoscopy under anesthesia after 1 month, while in group B, the external stent was removed after 5 to 7 days. We examined the durations of operation, hospital stay and the frequency of stent-related complications including urinary leakage, stent dislocation, stent occlusion, and urinary tract infection. RESULTS: The operation time was significantly longer for patients in group B than for those in group A. No significant difference was observed between the groups regarding stent-related complications. In group A, 4 patients need auxiliary stent re-insertion for the management of complications, 2 developed urinary tract infection, and 2 had stent occlusion. In group B, none needed auxiliary stent re-insertion for complications and avoided re-operation. CONCLUSIONS: In children, the outcome of external stent implantation was similar to that using double-J stent, and the use of the former approach may be beneficial for younger children.
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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 | 0.000 |
| 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) | 0.001 | 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".