High-Powered Holmium-Laser Ablation: Comparison of Setting Effectiveness Using Tissue-Surrogate Model
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The 100-W holmium laser, in conjunction with a sidefiring 550-microm fiber, can ablate prostate glands as large as 40 g to relieve lower urinary-tract symptoms. We evaluated the effect of various setting combinations on ablation efficiency using beef kidney as a tissue-surrogate model. MATERIALS AND METHODS: Beef kidney specimens (mean weight 44.8 +/- 3.1 g) were secured in a cylinder, which was submerged in a water-filled tank through which a 27F resectoscope and 550-microm sidefiring fiber were positioned. Four energy/frequency combinations were tested, with each used to treat 10 kidney specimens. The difference between the mean pretreatment and post-treatment weights of each treatment group were compared statistically with Student's t-test. RESULTS: The largest mean weight difference after treatment (8.94 +/- 2.38 g) was achieved using 3.2 J and 25 Hz. This mass reduction was significantly greater than that of all other combinations except 2.5 J and 40 Hz. CONCLUSIONS: Use of the 3.2 J and 25 Hz setting combination resulted in the greatest amount of ablation in this tissue-surrogate model, suggesting that maximal energy settings may provide an advantage in tissue vaporization using the 100-W holmium laser. Clinical assessment must be performed to substantiate these findings.
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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 | 0.000 |
| 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".