Endopyelotomy still has an important role in the management of ureteropelvic junction obstruction
Bibliographic record
Abstract
Advances in endourology have lead to a change in the management of primary ureteropelvic junction obstruction (UPJO) over the past 25 years. Minimally invasive procedures (endopyelotomy and laparoscopic pyeloplasty) have replaced open pyeloplasty as the standard of care. The benefits of these procedures include less postoperative pain, shorter hospital stay, faster return to normal activities and less morbidity.1–3 Laparoscopic pyeloplasty produces success rates (90% to 100%) equivalent to open pyeloplasty and has a 10% to 15% higher success rate when compared to endopyelotomy (antegrade or retrograde).4–6 For these reasons, many urologists consider laparoscopic pyeloplasty the treatment of choice for primary UPJO; however, the technical challenges associated with laparoscopy and intracorporeal suturing have limited its widespread use. Therefore, although laparoscopic pyeloplasty has become the new standard of care, endopyelotomy remains an effective alternative first-line treatment for certain patients with primary UPJO, and can be easily performed by most urologists without the need for advanced laparoscopic training. Additionally, it is the treatment of choice for failed open or laparoscopic pyeloplasty and concomitant renal calculi. We present the case that endopyelotomy still has a role in the management of UPJO in select patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".