Moses technology in a stone simulator
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to compare fragmentation efficiency of the conventional regular mode of holmium laser to the Moses contact mode on a stone simulator. METHODS: P120 H holmium laser system, together with Moses D/F/L fibers were used to compare regular mode with Moses contact mode in a stone simulator model using flexible ureteroscopy with artificial stones. Efficiency of laser lithotripsy was measured by procedural time. Degree of stone retropulsion was also compared between regular and Moses modes using a Likert scale from 0 (no retropulsion) to 3 (maximum retropulsion). RESULTS: Using the stone simulator model, a significant reduction in stone retropulsion was noticed when comparing regular mode to the Moses contact mode (mean Grade 2.5 vs. Grade 1; p<0.01). When compared with the regular mode, the Moses contact mode was associated with significantly shorter procedural time during fragmentation (13.9 vs. 9.1 minutes; p≤0.01) and dusting (9.3 vs. 7.1 minutes; p≤0.01). In addition, when compared with the regular mode, the laser pedal was pressed significantly less often with the Moses mode during fragmentation (86 vs. 43 times; p<0.01) and dusting (50 vs. 26 times; p<0.01). Moses contact mode was associated with significantly higher percentage of lasing vs. pausing when compared with the regular mode for both fragmentation (0.8 J/10 Hz) and pulverization (0.4 J/50 Hz) settings (both p<0.05). CONCLUSIONS: Using the stone simulator setup, Moses technology was associated with more efficient laser lithotripsy (shorter operative time) due to significantly reduced stone retropulsion.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".