Third Prize: The Impact of Fluid Environment Manipulation on Shockwave Lithotripsy Artificial Calculi Fragmentation Rates
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Studies have suggested that shockwave lithotripsy (SWL) stone fragmentation rates can be affected by characteristics of the fluid media surrounding the stone, although evidence to implicate the impact of urine specific gravity (SG) is limited and inconclusive. Our aim is to further explore the impact fluid media and SGs have on stone fragmentation using a variable focus lithotripter. MATERIALS AND METHODS: Artificial stones were presoaked for 24 hours in urine and then shocked in various fluid media including artificial urine (SG 1.010 control, 1.020, and 1.07), human pooled urine (HPU), degassed HPU, Pentastarch, 100% and 30% contrast, degassed 30% contrast, 100% ethanol, deionized water (dH(2)O), degassed dH(2)O, 5% glucose, Ringer lactate, 0.9% saline, glycerol, whole blood, and lubricating gel. After soaking, SWL using the Modulith SLX-F2 electromagnetic lithotripter was performed. Fragments were dried and sieved using a 4-mm diameter opening grid. Fragments >4 mm were weighed and fragmentation coefficients (FCs) calculated (pre-SWL weight - post-SWL weight)/(pre-SWL weight) × 100. Fifteen stones were shocked for each fluid group. RESULTS: Fluid type, viscosity, and degassing all significantly impacted stone fragmentation. While the solutions' SG, per se, did not appear to affect stone fragmentation, the use of degassed 30% contrast significantly improved stone destruction over the SG 1.010 artificial urine control (95.3% vs 71.4, P < 0.01). Furthermore, degassing improved comminution rates by increasing the number of completely fragmented stones (FC = 100%). Using degassed 30% contrast, 12/15 stones were completely fragmented, compared with only 2/15 in the control group (P = 0.007). Among the whole blood, glycerol, and lubricating gel groups, only 1/15, 0/15, and 1/15 stones reached 100% FC respectively in the narrow focus, possibly because of the detrimental impact of increased viscosity. CONCLUSIONS: Different fluid media can significantly affect FC in vitro. Among the various fluids tested, degassed 30% contrast significantly increased the FC and total number of completely fragmented stones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.009 |
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".