Ureteroscopic Laser Lithotripsy: A Review of Dusting <i>vs</i> Fragmentation with Extraction
Bibliographic record
Abstract
INTRODUCTION: Ureteroscopic laser lithotripsy is becoming the most commonly utilized treatment for patients with urinary calculi. The Holmium:YAG (yttrium aluminum garnet) laser is integral to the operation and is the preferred flexible intracorporeal lithotrite. In recent years, there has been increasing interest in examining the effect of varying the laser settings on the effectiveness of stone treatment. Herein, we review the two primary laser treatment approaches: dusting and fragmentation with extraction. METHODS: We reviewed PubMed and MEDLINE databases from January 1976 through January 2017. All authors participated in the development of consensus definitions of dusting and fragmentation with extraction. The review protocol adhered to preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. RESULTS: When the Holmium:YAG laser is used to treat stones, there are two parameters that can be adjusted: power (J) and frequency (Hz). In one treatment paradigm, which became termed "fragmentation with extraction," laser settings that relied on high energy and low frequency were used. Another paradigm, which became termed "dusting," utilized low energy and high frequency settings, which had the effect of breaking off exceedingly small fragments from the stone. CONCLUSIONS: Both dusting and fragmentation with extraction approaches to ureteroscopic stone treatment are effective. In fact, there is little evidence that one approach is better than the other. However, each does have relative advantages and disadvantages, which should be considered. Although dusting tends to be associated with shorter procedure times and a lower risk of ureteral damage, this approach may place the patient at increased risk for future stone events should all of the resultant debris not be expelled from the collecting system. The active removal associated with fragmentation with extraction, in contrast, may provide for a more complete initial stone clearance.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".