With great power comes great risk: High ureteral stricture rate after high-power, high-frequency Thulium fiber laser lithotripsy in ureteroscopy
Bibliographic record
Abstract
Abstract Purpose To compare the safety and efficacy of Thulium Fiber Laser (TFL) using either manufacturer presets (MP) or individualized presets (IP) in ureteroscopy. Methods Multi-institutional, retrospective analysis on the first patients treated with SOLTIVE ® Premium (Olympus Medical Systems ® ) TFL in Switzerland in 2020. MP were used at the University Hospital of Geneva, while IP were used at the University Hospital of Zurich. Patient demographics, stone characteristics, and procedural details were collected. Primary outcome was postoperative ureteral stricture (US). Secondary outcome was stone-free rate (SFR). Results A total of 158 patients were analyzed, 79 in each group. Demographics were similar between the two groups, except for a lower pre-stenting rate in the MP group (56% vs. 91%; p < 0.001) and a higher rate of ureteral access sheath use in the MP group (65% vs. 44%; p = 0.011). No significant differences in stone burden (median stone diameter 9 mm, median stone volume 267 mm 3 ), nor in the rate of impacted ureteral stones (29% vs. 34%; p = 0.49). Mean power, maximal power, frequency settings, and energy consumption were significantly higher in the MP group. US rate was 11% in MP group compared to 1% in IP group ( p = 0.009). MP were a significant predictor of US on multivariable analysis (OR 12.4; p = 0.02), independently from impacted ureteral stones. No difference in SFR between groups (85% and 84%; p = 0.67). Conclusion High-power, high-frequency laser settings from manufacturer laser presets increase the risk of US, without improving SFR. Future studies shall further evaluate optimal laser settings depending on patient characteristics and intraoperative situation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".