Prone Versus Supine Lasix Renal Scan to Assess Surgical Success After Laparoscopic and Robot-Assisted Pyeloplasty
Bibliographic record
Abstract
PURPOSE: Success after laparoscopic pyeloplasty (LP) for ureteropelvic junction obstruction is determined based on renal scan (RS) results and patient symptoms ± ultrasonography. The upright or prone position during RS may facilitate drainage. This study reports on outcomes after LP and robot-assisted pyeloplasty (RALP) and determines if patient position (supine vs prone) alters the results of the postoperative RS and surgical "success." PATIENTS AND METHODS: A retrospective review of LP and RALP performed by one surgeon between 2005 and 2012 was performed. Follow-up consisted of RS ± ultrasonography. The paired t test was used to assess for a significant difference between mean T1/2 for supine vs prone scans in each patient. Linear regression was used to determine if preoperative split renal function on the affected side or degree of preoperative hydronephrosis predicted difference in supine vs prone T1/2. RESULTS: There were 11 LP and 81 RALP performed; 84 had follow-up data. There were four (4.3%) failures. Thirty-eight patients had sufficient supine and prone RS for analysis. The difference in T1/2 between supine and prone RS was significant (mean difference 10.18 ± 27.28 min, P = 0.03). Strict success increased to 65.8% from 44.7% and combined strict plus technical success increased to 78.9% from 63.1% on prone vs supine RS. Split function and degree of hydronephrosis were not predictors of difference in RS results. CONCLUSIONS: LP and RALP have good technical results. Prone position for RS may facilitate drainage and may be a more accurate representation of postoperative outcome after pyeloplasty, particularly in equivocal cases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".