Shockwave Frequency Affects Fragmentation in a Kidney Stone Model
Bibliographic record
Abstract
PURPOSE: To study the effect of altering shockwave frequency on the efficiency of stone fragmentation using the MFL 5000 spark-gap lithotripter. MATERIALS AND METHODS: Standardized solid plaster stones, 12.0 +/- 0.5 mm in diameter, were fragmented at an energy setting of 20 kV. The shockwave frequencies tested were 60, 80, and 117 per minute. Stones were fragmented throughout the entire lifespan of the electrode, from 0 to >100% consumption, at each frequency tested. Electrode pressure output was studied for each frequency. RESULTS: A greater number of shocks was required to fragment the plaster balls at higher frequencies (regression coefficient 1.93; p < 0.003). An inverse relation was found between the number of shocks necessary to break the stones and electrode consumption (regression coefficient -2.16; p < 0.001). The analysis of delivered pressure from the electrode failed to demonstrate a linear relation with frequency (regression coefficient -0.40; p < 0.728) or consumption (regression coefficient -1.11; p < 0.158). CONCLUSIONS: The number of shocks required to fragment a stone is influenced in part by the frequency at which the shockwaves are delivered. Increasing the shockwave frequency from 60 to 117 per minute in this study caused a significant rise in the number of shocks required to break the stone. The pressure output of the electrode was similar at the frequencies tested, thus making the difference in stone fragmentation secondary to the mechanism of stone disintegration and not the function of the electrode.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".