Holmium: YAG lithotripsy varies with power settings.
Bibliographic record
Abstract
The holmium:YAG laser fragments stones by photothermal mechanism. Increased pulse energy (PE) produces larger ablation craters, implying faster lithotripsy. However, increased PE increases retropulsion, implying slower lithotripsy. Optimal power settings were studied. Uniform stone phantoms were ablated in water (500 J total energy). Six power settings were tested: ranging from 0.2 to 2.0 at 10–40 Hz. Two conditions were tested: no stabilization vs stabilization devices placed behind the stone. Total fragmentation (TF) and fragment sizes were quantified. In the no stabilization cohorts, retropulsion was measured. Pressure transients were measured by needle hydrophone. Stone crater volumes were quantified by optical computed tomography. With or without stabilization, TF increased as PE increased, p<0.0001; and fragment size increased as PE increased, p<0.05. Without stabilization, retropulsion increased as PE increased, p<0.0001. TF was greater with vs without stabilization, p<0.01. Pressure transients were <30 bars even at 2.0 J. Crater volumes increased as PE increased, p<0.01 but remained symmetric. Increased PE produces more lithotripsy but also larger fragments. Even at high PE (2.0 J) Ho:YAG lithotripsy is photothermal. Low PE produces small fragments but less lithotripsy. Modest PE (0.2–0.5 J) at high-repetition rate produces more fragmentation, small fragments, and less retropulsion. [Work supported by Percsys and Boston Scientific.]
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".