Laser access and utilization preferences for pediatric ureteroscopy: A survey of the Societies of Pediatric Urology
Bibliographic record
Abstract
INTRODUCTION: We sought to evaluate laser access and practice variability for pediatric ureteroscopy (URS) across the Societies of Pediatric Urology (SPU) to identify opportunities and barriers for future technology promulgation and evidence dissemination. METHODS: A 25-question survey was sent electronically to members of the SPU. The questionnaire assessed surgeon and hospital characteristics, treatment preferences based on an index case, and information about available laser units. Descriptive and comparative statistical analyses were performed to assess patterns of care and laser accessibility across the SPU. RESULTS: A total of 105 of 711 (15%) recipients responded. Seventy-seven respondents (73%) reported laser ownership, which was associated with greater after-hours laser access (87% vs. 13%, p<0.01). Fifty-eight individuals provided additional laser specifications, of whom 21 (36%) used a high-powered laser unit (>60 W). Standard-power lasers were used more frequently in free-standing children's hospitals, as compared to those working within a larger hospital complex (75% vs. 50%, p=0.049). Variation existed in treatment preferences with respect to dusting (33, 34%), fragmentation (18, 19%), or a hybrid approach (46 respondents, 48%). Stone clearance was the most important consideration irrespective of treatment choice. CONCLUSIONS: Variability in surgical preferences and accessibility to laser units exist across pediatric urologists who perform URS. Laser ownership and access to newer technologies vary across practices and may influence treatment options. Understanding access to laser technology will be important when considering opportunities for surgical optimization to improve patient outcomes through future studies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".