<i>In Vitro</i> Evaluation of LithAssist: A Novel Combined Holmium Laser and Suction Device
Bibliographic record
Abstract
INTRODUCTION: The aim of this in vitro study was to evaluate and compare a novel intracorporeal lithotripter, LithAssist (LA; Cook Medical, Bloomington, IN), with the Swiss LithoClast Ultra (SLU; Boston Scientific, Boston, MA) for the fragmentation and removal of artificial stones made of gypsum-based cement. MATERIALS AND METHODS: Ten soft and 20 hard ultracal-30 (U-30) stones were fragmented using two lithotripters. We recorded the stone weight (grams) prior to placing them into a 60-mL syringe for fragmentation. We inserted a 30F percutaneous access sheath into the syringe and positioned the stone within its lumen. Next, we inserted the lithotripter into a right-angled nephroscope. We recorded the times required for first and complete stone disintegration, disintegration to 2 mm, and complete stone removal for each device. In addition, we recorded the stone mass following each minute of stone fragmentation. RESULTS: In total, we subjected 5 soft and 10 hard stones to SLU and LA, respectively. All soft stones were completely disintegrated and removed with both the SLU and LA device. For soft stones, disintegration to 2 mm (2.83±0.41 vs. 4.15±0.70 minutes, p=0.049), complete disintegration (3.18±0.20 vs. 6.40±1.95 minutes, p=0.038), and complete removal (3.30±0.22 vs. 8.82±1.05 minutes, p=0.001) were faster for the SLU compared with the LA. For hard stones, fragmentation was not accomplished with the SLU, whereas with the LA, mean time for first disintegration, disintegration to 2 mm, complete disintegration, and complete removal was 3.60±1.36, 7.25±3.33, 7.54±2.94, and 8.64±2.78 minutes, respectively. CONCLUSIONS: In this in vitro study, the SLU was more efficient for softer artificial stones, and the LA was more efficient for harder artificial stones.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".