Benchtop and Initial Clinical Evaluation of the ShockPulse Stone Eliminator in Percutaneous Nephrolithotomy
Bibliographic record
Abstract
PURPOSE: Standardized bench testing of the new ShockPulse™ intracorporeal lithotripter was performed against three commercially available lithotripsy systems to determine differences and nuances in performance. MATERIALS AND METHODS: The ShockPulse intracorporeal lithotripter was tested against the LUS-2™, CyberWand,™ and EMS LithoClast™ in a standardized bench setting using hard (Ultracal-30) and soft (plaster of Paris) stone phantoms. An in vitro kidney model was used to record the time needed to fragment stone samples into retrievable-sized pieces. The time needed to fully comminute and evacuate stone samples was also recorded. The efficacy of each device at various applied pressures was determined using a hands-free apparatus, which was used to apply 1.0, 1.5, and 2.0 pounds of fixed force. RESULTS: For hard and soft stones, the time needed to create retrievable fragments was similar among all systems (p = 0.585). The ShockPulse was significantly faster than the LUS-2 and LithoClast at fully fragmenting and evacuating stone samples (p = 0.046), while the CyberWand was significantly slower than all three systems at this task (p = 0.001). When fixed forces were applied to a large stone phantom, the ShockPulse and CyberWand were significantly faster than the LUS-2 and LithoClast (p < 0.0001). When groups of smaller stones were tested, the ShockPulse was significantly faster at 1.0 pound (p < 0.001) and 1.5 pounds (p < 0.002) of force. At 2.0 pounds, no differences were observed (p = 0.09). CONCLUSIONS: The ShockPulse is equally as effective and, in some circumstances, more effective than the three commercially available devices against which it was tested in an in vitro setting.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".