Can negative ureteroscopy be predicted in ureteral stone treatment?
Bibliographic record
Abstract
INTRODUCTION: We aimed to evaluate factors predictive of negative ureteroscopy (URS) in ureteral stones. METHODS: Patients who underwent URS between January 2007 and June 2018 were included in the study. Patients were divided into two groups: group 1- positive URS (841 patients); and group 2 -negative URS (75 patients). These two groups were compared in terms of demographic data, stone characteristics, and postoperative outcomes. RESULTS: The mean age of the study patients was 44.5±15.1 years. The absence of collecting system dilatation due to the present stone was found to be a significant predictive factor for negative URS in univariate analysis, but there was no significant difference in multivariate analysis. In the multivariate analysis, low body mass index (BMI), no history of stone surgery, stone located in the distal ureter, small stone area, longer time between the last imaging procedure and URS, and medical expulsive therapy (MET) application were statistically significant in predicting negative URS. CONCLUSIONS: In this study, the parameters that significantly predicted negative URS were found to be low BMI, no history of stone surgery, distal localization of the stone, small stone area, longer time between the last imaging procedure and URS, and MET applied for the current stone. These parameters should be considered to avoid negative URS and patients should be informed of the possibility of negative URS prior to operation.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".