Ureteral Reimplantation for Primary Obstructive Megaureter in Pediatric Patients: Is It Time for Robot-Assisted Approach?
Bibliographic record
Abstract
Objective: To compare open and robotic approach for treatment of Primary Obstructive Megaureter (POM) in a series of pediatric patients. Materials and Methods: Medical records of all patients who had undergone ureteral reimplantation for POM at our institution, between January 2016 and December 2019, were retrospectively collected and analyzed. Open (Group B) and robotic (Group A) approach were compared. Success was defined as reduction of anteroposterior diameter of pelvis and ureter on postoperative ultrasound scan. Surgical complications were reported according to the Clavien-Dindo Classification. Esthetic results were compared through the Vancouver Scar Scale. Results: Out of 23 patients, 11 belonged to Group A (5M and 6F) and 12 to Group B (9M and 3F). Median age at diagnosis was 38 months in Group A and 46 months in Group B (P = .69). Operative times were comparable (150’ Group A and 140’ Group B; P = .35), but the hospital stay was significantly shorter in the robotic group (P < .01). Success rate was comparable: 90.9% in Group A and 91.7% in Group B, respectively (P = .95). Incidence of early complications was 9.1% in Group A (grade IIIB) and 8.3% in Group B (grade II) (P = .95). Esthetic evaluation obtained a median score of 1 in Group A and 3 in Group B (P < .01). Conclusions: Our preliminary experience shows that robotic reimplantation can be a safe option in the treatment of POM in pediatric patients with clinical outcomes comparable to the open technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".