Increased oscillation rate may improve morcellation efficiency in HoLEP
Bibliographic record
Abstract
INTRODUCTION: Tissue morcellation has become increasingly efficient, yet remains a rate-limiting step in holmium enucleation of the prostate (HoLEP). Limited data exists on how the rate of oscillation by the morcellator blades affects morcellation efficiency (ME). METHODS: We undertook a retrospective review of HoLEP procedures performed by two surgeons from July 1, 2019, to August 25, 2022. All morcellation was performed with the Wolf Piranha device and enucleation was performed with Moses 2.0 technology. Surgeon 1 routinely uses 1500 oscillations/min (low rate [LR]) and surgeon 2 uses a rate of 6000 oscillations/min (high rate [HR]). These rates were confirmed upon electronic medical record review of each case. The primary endpoint was ME (g/minute). Secondary endpoints included enucleation efficiency (EE), mean tissue specimen weight, and preoperative prostate volume. RESULTS: A total of 894 HoLEPs were analyzed, 592 by surgeon 1 and 302 by surgeon 2. Surgeon 1 had larger preoperative prostate volumes (126 vs. 101, p<0.001) and specimen tissue weights (86.0 vs. 61.1, p<0.001). EE was higher in the LR group (1.67 vs. 1.33 g/min, p<0.001). Morcellation time was longer in the LR group (11.3 vs. 6.09 min, p<0.001) and ME was lower in the LR group (9.26 vs. 12.1 g/min, p<0.001). The difference in ME was inversely proportional to specimen weight. CONCLUSIONS: Increased oscillation rate during morcellation may lead to decrease in morcellation time and increased ME during prostate enucleation. The primary limitation of this paper is the inclusion of only two surgeons. Future studies will serve to evaluate this finding across a larger number of institutions, and evaluate ways to increase ME in large prostate cohorts.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| 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".