Understanding failures in getting it up: The prevalence and predictors of failed ureteral access in ureteroscopy
Bibliographic record
Abstract
INTRODUCTION: Failed access ureteroscopy (FA) describes the inability to gain adequate access to a stone to allow for treatment. The purpose of this study was to identify the prevalence of, and factors predicting FA in patients presenting with renal and ureteral stones. METHODS: We conducted a retrospective review of all ureteroscopy (URS) procedures performed for renal and ureteral stones by three endourologists over a six-month period at our center. All patients who underwent URS for the purpose of stone treatment were included. Patients were excluded if they underwent URS for non-stone diagnosis or treatment. FA was investigated in relation to demographics, medical history, stone-specific characteristics, procedure-specific characteristics, etc. Statistical analysis consisted of descriptive statistics, as well as Chi-squared and t-test analysis using SPSS statistical software version 24.0. RESULTS: A total of 188 cases were reviewed, with 8% of patients experiencing FA. Patient age, gender, body mass index (BMI), American Society of Anesthesiologists (ASA) score, emergency cases, previous stone treatment, use of computed tomography (CT) imaging, presence of hydronephrosis, and surgeon did not differ significantly between FA and successful access (SA) groups. Stone size (9.88±5.8 vs. 8.76±4.3 mm; p=0.361) was also not significantly different. However, a significant difference was noted in time from first diagnosis to URS (128 vs. 65 days, p=0.044) between the FA and SA groups, respectively. Similarly, for ureteral stones, the FA group had a significantly greater proportion of stones located in the proximal ureter (62.5% vs. 22.0%, p=0.043). CONCLUSIONS: Proximal ureteric stones were more likely to result in FA URS, and FA procedures were more likely to be preceded by extended time from first diagnosis to URS. Further investigation is necessary, and all endourology centers should track their own personal outcome data to allow for more meaningful analysis to be performed to improve patient outcomes.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".