Long surgical waiting list times are associated with an increased rate of negative ureteroscopies
Bibliographic record
Abstract
INTRODUCTION: Negative ureteroscopy (NURS) is "a ureteroscopy in which no stone is found during the procedure." We aimed to determine the association between the surgical waiting list time (WLT) and the NURS rate. METHODS: We retrospectively analyzed all patients scheduled for ureteroscopy in our center between January 2017 and July 2019. The inclusion criterion was unilateral, semirigid ureteroscopy for a single ureteral stone; exclusion criteria were renal-only stones, incomplete ureteroscopy, and stones >10 mm. We analyzed age; gender; body mass index; stone size, density, and location; presence of a temporary double-J (DJ) stent; use of medical expulsive therapy; and WLT. Complications while waiting for surgery were also collected and analyzed. RESULTS: We included 219 patients, 41 (18.7%) of whom had NURS. The median WLT was 74 days (interquartile range [IQR] 45-127). Variables protective against NURS were large stone size (odds ratio [OR] 0.78, 95% confidence interval [CI] 0.66-0.93), presence of a temporary DJ stent (OR 0.43, 95% CI 0.2-0.8), and radiopaque stones (OR 0.44, 95% CI 0.21-0.88). A long WLT ((≥60 days) increased the risk of NURS (OR 2.18, 95% CI 1.02-4.61). Complications requiring emergency department visits while waiting for surgery were documented in 58/137 (42.3%) patients with indwelling DJ stents; nonetheless, a WLT greater than the median was not associated with an increased risk of complications (p=0.38). CONCLUSIONS: Long WLT has an independent, direct, and linear correlation with NURS rates. Patients at higher risk of NURS, may be offered preoperative re-evaluation with a computed tomography scan in a resource-limited setting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".