Increased ablation efficiency in hard and soft tissues using an annular beam
Bibliographic record
Abstract
Lasers are commonly employed in surgery for hard and soft tissues due to their precise space-time energy delivery and compatibility with optical fibers for delivery into body cavities, including for treatment of urological diseases. Infrared laser ablation in tissues can result in non-specific heating and thermal injury. Methods that maximize ablation efficiency, or tissue volume removed per unit energy, while minimizing non-specific thermal injury can improve surgical workflows and outcomes. We report a novel approach for increased ablation efficiency by modifying the beam shape. Specifically, a Ho:YAG laser is shaped into a converging annular beam. Ablation efficiency was measured on a hard tissue phantom (BegoStone) and soft tissue (porcine kidney). An annular beam ~800 μm in diameter was used to ablate each sample at 10 different locations using a single 1 J pulse per location. The procedure was repeated using a circular beam with similar diameter by placing a 200 μm fiber 1 mm from the tissue surface. Each ablation crater was imaged with optical coherence tomography and the crater volumes calculated from recorded images. For hard tissue phantoms, ablation efficiency increased 183% for annular vs. circular beams (0.065±0.013 vs. 0.023 ± 0.003 mm<sup>3</sup> /J). For soft tissue, ablation efficiency increased 69% for annular vs. circular beams (0.098±0.021 vs 0.058 ± 0.018 mm3 /J). Hard and soft tissue ablation with an annular beam is a promising technique for increasing the speed and safety of laser surgery.
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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 | 0.001 | 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 | 0.001 |
| 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".