Effect of Pulse Modulation on Diode-Pumped Laser Lithotripsy
Bibliographic record
Abstract
Introduction: This study addresses pulse modulation for kidney stone lithotripsy using diode-pumped thulium yttrium aluminum garnet (Tm:YAG, λ = 2.02 µm) and thulium fiber lasers (TFLs, λ = 1.94 µm). Three research questions were investigated: (1) What are the effects of varying pulse duration and energy of the first pulse and varying the interpulse delay in a pulse modulation sequence to increase energy transfer across saline to a stone? (2) Does an optimal pulse modulation profile exist in a single-pulse sequence to provide highest percent radiant energy transfer? (3) Does a higher effective energy transfer to the stone using pulse modulation produce greater stone volumetric removal? Materials and Methods: We measured radiant energy transmission efficiency (RETE) and ablation volumes in phantom and human stones. RETE was utilized to compare the pulse energy transmission through air and saline media. We recorded fast camera traces and vapor bubble collapse pressures. Craters were created at fiber standoff distances (SDs) of 0.0 mm, 0.5 mm, and 1.0 mm, and volumes were measured using optical coherence tomography. Results: For Tm:YAG laser irradiation, dual-pulse mode significantly increased RETE by as much as 75% at 1 mm SD compared with single-pulse mode. With the Tm:YAG laser, an optimal “dual-pulse” modulation resulted in greater volumetric removal compared with a “single-pulse” across all stone cohorts ( p < 0.05) except for calcium oxalate monohydrate stones ( p = 0.38) at a 1 mm SD. TFL yielded similar results, but showed heterogeneity across stone compositions. Conclusions: Pulse-modulated diode-pumped Tm:YAG and TFL can deliver higher photon count through a saline layer if the first pulse is optimized. An optimal pulse modulation profile where the second pulse is synchronized with the vapor bubble dynamics of the first pulse results in the highest percent RETE and increased stone volumetric removal.
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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) | 0.000 | 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) | 0.001 | 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".