Thulium fiber laser lithotripsy: Is it living up to the hype?
Bibliographic record
Abstract
The holmium:yttrium-aluminium-garnet laser (Ho:YAG) has been the gold standard for laser lithotripsy over the last three decades. After demonstrating good in vitro efficacy, the thulium fiber laser (TFL) has been recently released in the market and the initial clinical results are encouraging. This article aims to review the main technology differences between the Ho:YAG laser and the TFL, discuss the initial clinical results with the TFL as well as the optimal settings for TFL lithotripsy. We reviewed the literature focusing on the technological aspects of the Ho:YAG laser and TFL as well as the results of in vitro and in vivo studies comparing both technologies. In vitro studies show a technical superiority of TFL compared to the Ho:YAG laser and encouraging results have been demonstrated in clinical practice. However, as TFL is a new technology, limited studies are currently available, and the optimal settings for lithotripsy are not yet established. TFL has the potential to be an alternative to the Ho:YAG laser, but more reports are still needed to determine the optimal laser for lithotripsy of urinary tract stones when considering all parameters including effectiveness, safety, and costs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".