Urinary calculus fragmentation during Ho: YAG and Er:YAG lithotripsy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: We tested Ho:YAG and Er:YAG laser ablation of human urinary calculi to determine if Er:YAG is a more efficient lithotripsy device. STUDY DESIGN/MATERIALS AND METHODS: Ablation efficiency of Ho:YAG and Er:YAG lasers was tested at varying energy settings, ranging from the damage threshold to clinical energy setting associated with Ho:YAG laser. Stones of known composition (calcium oxalate monohydrate (COM), cystine, and uric acid (UA)) were irradiated. Crater width, depth, and ablation volumes were determined using an optical coherence tomography (OCT). RESULTS: For all stones and energy settings, the Er:YAG laser produced deeper craters and larger ablation volumes than Ho:YAG laser. The Ho:YAG laser created wider craters during the multiple pulse process and the shape of craters was irregular. CONCLUSIONS: The Er:YAG laser is more efficient than the Ho:YAG laser for lithotripsy. The deeper craters produced by the Er:YAG laser is attributed to the high absorption of energy at its wavelength.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".