Comparing dusting and fragmenting efficiency using the new SuperPulsed thulium fiber laser versus a 120 W Holmium:YAG laser
Bibliographic record
Abstract
PURPOSE: Holmium:YAG laser lithotripsy requires high amperage power and has an upper limit of frequency and a minimal fiber size. The technology utilizing thulium-doped fiber offers low pulse energy settings and high pulse frequencies up to 2,400 Hz. We compared the novel SuperPulsed thulium fiber laser (SOLTIVE™; Olympus) to a commercially available 120 W Ho:YAG laser. MATERIALS AND METHODS: standardized BegoStones (Bego USA). Time to ablate the stone into particles <1 mm was recorded for efficiency calculations. Finite energy was delivered, and resulting particle sizes were measured to determine fragmentation (0.5 kJ) and dusting (2 kJ) efficiencies. Remaining mass or number of fragments were measured to compare efficacy. RESULTS: SOLTIVE™ was faster at ablating stones to particles <1 mm (2.23±0.22 mg/s, 0.6 J 30 Hz short pulse) compared to Ho:YAG laser (1.78±0.44 mg/s, 0.8 J 10 Hz short pulse) (p<0.001). Following 0.5 kJ of energy in fragmentation testing, fewer particles >2 mm remained using SOLTIVE™ than Ho:YAG laser (2.10 vs. 7.20 fragments). After delivering 2 kJ, dusting (1.05±0.08 mg/s) was faster using SOLTIVE™ (0.1 J 200 Hz short pulse) than 120 W 0.46±0.09 mg/s (0.3 J 70 Hz Moses) (p=0.005). SOLTIVE™ (0.1 J 200 Hz) produced more dust particles <0.5 mm (40%) compared to 24% produced by the P120 W laser at 0.3 J 70 Hz Moses and 14% at 0.3 J 70 Hz long pulse (p=0.015). CONCLUSIONS: The efficacy of SOLTIVE™ is superior to the 120 W Ho:YAG laser by producing smaller dust particles and fewer fragments. Further studies are warranted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".