Comparison Between Holmium:YAG Laser with MOSES Technology vs Thulium Fiber Laser Lithotripsy in Retrograde Intrarenal Surgery for Kidney Stones in Adults: A Propensity Score–matched Analysis From the FLEXible Ureteroscopy Outcomes Registry
Bibliographic record
Abstract
PURPOSE: We evaluated stone-free rate and complications after flexible ureteroscopy for renal stones, comparing thulium fiber laser and holmium:YAG laser with MOSES technology. MATERIALS AND METHODS: Data from adults who underwent flexible ureteroscopy in 20 centers worldwide were retrospectively reviewed (January 2018-August 2021). Patients with ureteral stones, concomitant bilateral procedures, and combined procedures were excluded. One-to-one propensity score matching for age, gender, and stone characteristics was performed. Stone-free rate was defined as absence of fragments >2 mm on imaging within 3 months after surgery. Multivariable logistic regression analysis was performed to evaluate independent predictors of being stone-free. RESULTS: < .001). At multivariable analysis, the use of thulium fiber laser and ureteral access sheath ≥8F had significantly higher odds of being stone-free. Lasing time, multiple stones, stone diameter, and use of disposable scopes showed significantly lower odds of being stone-free. CONCLUSIONS: This real-world study favors the use of thulium fiber laser over holmium:YAG laser with MOSES technology in flexible ureteroscopy for renal stones by way of its higher single-stage stone-free rate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".