Prospective evaluation of efficacy, safety, cumulative laser energy, and stone-free rates in the post-market SOLTIVE™ SuperPulsed laser system registry: insights from team of worldwide endourological researchers’ (T.O.W.E.R.) research consortium
Bibliographic record
Abstract
The Thulium fiber laser (TFL) is a relatively new tool for endoscopic laser lithotripsy. The Endourological Society’s T.O.W.E.R. registry sought to evaluate the stone-free rate (SFR) at 3 months following URS. A subset of the study sought to determine the association between cumulative TFL energy and SFRs. 423 patients with planned ureteroscopic lithotripsy using TFL (SOLTIVE ™ , Gyrus ACMI, Inc. d/b/a Olympus Surgical Technologies America) were prospectively enrolled between December 2020 and May 2023 at nine international sites. Baseline clinical characteristics and SFR data for kidney and ureteral stones were separately analyzed according to quartile cumulative TFL energy ranges. Median patient age was 58.0 (IQR: 44–67) years and maximal stone diameters were 9.9 (IQR: 7–12.9) mm and 7.4 (IQR 6.1–9.4) mm for kidney and ureteral stones, respectively. Overall SFR (no fragments) for renal and ureteral stones were 73.0% and 85.7% at 3-months. Cumulative energy levels were divided into quartiles and lower SFRs were observed with the highest quartile for kidney stones ( p = 0.001), but not in ureteral stones. This correlated with kidney stone size as larger stones required more energy. The rate of adverse events related to the procedure was 1.9% (8/423). Higher stone burdens had lower stone free rates and required more cumulative laser energy. The TFL is effective in endoscopic lithotripsy. This post-marketing survey demonstrates that TFL is a safe and effective tool for endoscopic laser lithotripsy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".