Wolf Piranha <i>vs</i> Storz Prostate Morcellation Devices: A Retrospective Multi-Institutional Study
Bibliographic record
Abstract
Purpose: Holmium laser enucleation of the prostate (HoLEP) entails both enucleation and morcellation. Only three popular prostate morcellation devices are available for this procedure. In this study, a retrospective review was done to compare the Wolf ® and Storz ® morcellators. Materials and Methods: After Institutional Review Board approval, a multi-institutional retrospective chart review of prospectively collected data was performed at two institutions with a single surgeon at each center performing HoLEP. Thunder Bay Regional Health Sciences Center employed the Storz morcellator while Baylor Scott and White Medical Center used the Wolf. Preoperative, perioperative, postoperative, and demographic data for both sets of patients were analyzed and compared retrospectively. Results: A total of 506 patients in the Wolf cohort and 60 patients in the Storz cohort were analyzed. Morcellated pathologic weight was 52.3 g in the Wolf arm and 101.7 g on the Storz arm ( p < 0.0001). Overall, average morcellation rates were faster in the Storz arm; morcellation rate was 5.8 g/min for Wolf, and 6.7 g/min in the Storz ( p = 0.0015). Morcellator malfunction was significantly lower in Wolf cohort 0% vs 6.6% in the Storz ( p = 0.0001), but this did not significantly slow morcellator efficiency times. The total number of mucosal bladder injuries was comparable with rates of 1.4% and 1.6% in the Wolf and Storz groups, respectively ( p = 0.59). The duration of hospital stay and catheterization were <24 hours in both groups. Conclusions: In this retrospective study, the Storz DrillCut had higher efficacy in morcellation when compared with Wolf Piranha. However, it was associated with more malfunctions. Both morcellators have comparable rates of complications and perioperative outcomes.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".