Use of the Moses Technology to Improve Holmium Laser Lithotripsy Outcomes: A Preclinical Study
Bibliographic record
Abstract
OBJECTIVE: To evaluate in vitro and in vivo effects of Moses technology in Holmium laser and to compare it with the Regular mode in terms of lithotripsy efficiency and laser-tissue interactions. METHODS: Pulse™ P120H holmium laser system together with Moses D/F/L fibers were used to compare the Regular mode with the Moses modes in stone retropulsion by using a high-speed camera, and stone ablation efficiency. In addition, a porcine ureteroscopy model was used to assess stone fragmentation and dusting as well as laser-tissue interaction with the ureteral wall. RESULTS: After a laser pulse, in vitro stone displacement experiments showed a significant reduction in retropulsion when using the Moses mode. The stone movement was reduced by 50 times at 0.8 J and 10 Hz (p < 0.01). The pronounced reduction of retropulsion in the Moses mode was clearly observed during fragmentation setting (high energy) and dusting (low energy, high Hz). In addition, stone fragmentation tests showed that the Moses modes resulted in a significantly higher ablation volume when compared with the Regular mode (160% higher; p < 0.001). In vivo assessment also supported the reduction in retropulsion when treating stones in the porcine kidney. Histological analysis of the porcine ureter after direct lasing in the Moses mode suggested less damage than in the Regular mode. CONCLUSIONS: The Moses technology resulted in more efficient laser lithotripsy, in addition to significantly reduced stone retropulsion, and displayed a margin of safety that may result in a shorter procedural time and safer lithotripsy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".