It’s Getting Hot in There: In Vitro Study on Ureteral Tissue Thermal Profiles During Laser Ureteral Lithotripsy
Bibliographic record
Abstract
Introduction: The integration of laser technology in urologic interventions, especially ureteral lithotripsy, has greatly advanced the field, with laser lithotripsy becoming the preferred method for treating ureteric stones via ureteroscopy. Recent advancements focus on enhancing power settings and reducing operating times, introducing high-power laser equipment capable of frequencies up to 120 Hz. However, concerns arise regarding thermal injuries to adjacent tissues due to increased energy delivery, potentially causing ureteric strictures. Objective: To explore temperature dynamics during ureteroscopic laser lithotripsy, considering factors like laser power settings and ureteroscope size, to optimize outcomes and mitigate risks for patients. Methods: A simulated in vitro model for ureteroscopic laser lithotripsy was designed with a holmium laser. Measurements of the temperature were recorded using a thermocouple placed at the laser tip at different sizes of ureteroscope (URS 6.0 Fr and URS 7.0 Fr), holmium laser (272 µm and 365 µm), various power settings (5 to 25 Hz; 0.2 to 3.0 J) and activation durations (3 to 30 s). Analysis of the variables associated with temperature change was performed. Results: All of the variables showed rising temperature trends as the laser activation time was prolonged, while ureteroscope size had no significant impact. Smaller laser fibers exhibited lower overall temperature profiles, around 34–35 °C. Notably, power settings significantly influenced temperature, with a substantial rise at 20 W (42.62 °C) and 30 W (40.02 °C). There was a significant rise in temperature as power (J × Hz) increased, where frequency carries a higher effect than energy at the same power setting. Conclusions: The recommendation includes exercising caution with higher power levels, shorter activation times, and preferably using small-caliber laser fibers to maintain lower temperatures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".