Dual usage of a stone basket: Stone capture and retropulsion prevention
Bibliographic record
Abstract
INTRODUCTION: Stone migration during ureteroscopy (URS) for proximal ureteric calculi is a constant challenge. Several retropulsion prevention devices have been developed to optimize URS outcomes. Our technique involves capturing the stone within a four-wire Nitinol stone basket and then performing laser lithotripsy to dust the stone while it is engaged in the basket. The dusted fragments wash out with the irrigation fluid and once small enough, the remaining stone is removed intact. METHODS: A retrospective chart review was performed of all proximal semi-rigid URS procedures for a solitary calculus (2000-2016). We compared our new technique introduced in 2010 to URS control procedures that did not use retropulsion prevention techniques or devices. RESULTS: One hundred and forty patients underwent URS for proximal ureteric calculi. Mean stone diameter was 9.3±3.4 mm, with similar impaction rate between both groups (44.1% vs. 43.1% control; p=n/s). The mean surgical procedure time was 53.3±17.9 minutes for the new technique and 65.2±29.2 minutes for the control group (p=0.005). Compared to the new technique, the control group had a higher rate of retropulsion (33.3% vs. 14.7%; p=0.01) and required flexible URS more often to exclude or remove residual fragments (24.1% vs. 59.1%; p=0.001). Using the new technique, stone-free rates were higher (79.1% vs. 69.4%; p=n/s) and there was a lower likelihood of leaving residual fragments both <3 mm and ≥3 mm (p=0.001). CONCLUSIONS: Our novel technique results in shorter operative times, lower retropulsion rates, and decreases postoperative residual stone fragments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".