A Novel Method for GreenLight MoXy Laser Fiber Irrigation System to Improve Performance and Durability: A New Standard of Care?
Bibliographic record
Abstract
Introduction and Objectives: The GreenLight™ MoXy ® laser fiber has been used since 2010 for benign prostatic hyperplasia procedures. We tested a novel principle to increase the saline irrigation flow rates beyond the current standard of gravity drip (∼22 cc/minutes) within the fiber-attached cooling system to potentially prevent excessive tissue adherence and to eliminate the likelihood of degradation due to abnormal overheating. The objective was to assess differences between the ordinary and active pumping methods with ≥2 times flow rate after conditioning of the laser fiber. Materials and Methods: A controllable full, tissue-contact system was utilized for conditioning in a porcine model, applying 180 W of vaporization mode of GreenLight XPS console for 30 continuous minutes. Four groups were evaluated using different saline flow rates; the nominal flow rate (control group, 22 mL/minute), digital pump set (35 mL and 50 mL/minute), and a manual pressure cuff with hand pump set using a 3-L saline bag with pressure of 300 mmHg (35–80 mL/minute). At the end of the conditioning process, a mechanical pull force test was executed on the fiber metal cap to evaluate the bonding strength. A failed event was defined as the natural detachment of the metal cap during the conditioning process or a cap pull force smaller than 22.24 N resulting in detachment. Additional physical parameters, including fiber tip temperature information and laser beam power transmission efficiency, were analyzed. Results: Detachment of the cap occurred less frequently when using the 300 mmHg pressure cuff saline bag compared to the nominal flow rate (6.67% vs 50%, respectively). The average operating fiber tip temperatures were lower in the higher flow rate groups compared to nominal, measured at 315°C and 305°C. compared to 442°C. Moreover, a significantly lower FiberLife Event count and an ∼5% increase of the average final laser transmission efficiency were observed in the higher flow rate groups. Conclusions: Our study demonstrates superior results when using active pumping or high-pressure systems to increase saline flow rates in terms of laser fiber durability without any additional cost. More specifically, use of a manual pressure cuff with starting pressure at 300 mmHg, a system that is readily available in most operating rooms, increases MoXy fiber durability. Further studies are required to assess if this technique will improve user experience, clinical outcomes, and procedure costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".