Assessing the Magnitude of Effect of Bone Structures on Shockwave Lithotripsy Fragmentation: Results from an <i>In Vitro</i> Study
Bibliographic record
Abstract
INTRODUCTION: Several anatomic and clinical factors have been implicated in the failure rates of shock wave lithotripsy (SWL), including the attenuating effects of bony structures. We designed an in vitro model that incorporates the lumbar spine, including vertebral bodies and transverse processes along the pathway of shockwaves, to mimic the clinical scenario during SWL of upper ureteral stones. We hypothesized that the presence of bone structures in the SWL pathway significantly affects the fragmentation rate. MATERIALS AND METHODS: An ordnance gelatin (OG) model was conceptualized and created to allow a pig's lumbar spine to be embedded within it. Artificial urinary calculi weighing 2 ± 0.1 g (1.2-cm diameter) were prepared using BegoStone plaster. The trial was divided into two arms: group 1 models had OG only and served as the control and group 2 models had the bone embedded in the gelatin with stone wells placed above the transverse processes. Twenty-four stones per group were subjected to SWL using the STORZ MODULITH SLX-F2 lithotripter, using the same treatment parameters. Fragments were sieved through 2- and 4-mm filters, and the fragmentation coefficients (FC) were calculated. The Mann-Whitney test was used to compare FC between the two groups. RESULTS: The mean fragmentation rate of group 1 was statistically significantly higher compared with group 2 using a 4-mm sieve (43% vs 0.62%, p < 0.001) and the 2-mm filter (18% vs 0.52%, p < 0.001). CONCLUSIONS: The presence of bone structures dramatically reduces the fragmentation rate of phantom stones using an OG in vitro model. The OG model is inexpensive and simple to use to simulate clinical situations during SWL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".