Ureteric wall thickness as a novel predictor for failed retrograde ureteric stent placement
Bibliographic record
Abstract
INTRODUCTION: We sought to identify predictors of failed retrograde ureteric stent (FRS) placement in the setting of obstructing ureteric calculi. In addition to patient- and stone-specific characteristics, we also considered computed tomography (CT) measures of ureteric wall thickness (UWT), as it has shown clinical potential in predicting outcomes of shockwave lithotripsy, ureteroscopy, and spontaneous stone passage. METHODS: We performed a retrospective, case-control study comparing patients who had successful retrograde stent (SRS) insertions with those who failed stent placement and ultimately required nephrostomy tube (NT) insertion (2013-2019). Patients were identified using administrative data from a shared electronic medical record (capturing all urology patients in our geographic area) and a prospective database capturing all institutional interventional radiology procedures. Patient demographics, as well as clinical and stone characteristics, were then collected, and imaging manually reviewed. Statistical analysis was performed using univariate and multivariate logistic regression analysis in collaboration with a statistician. RESULTS: A total of 109 patients met inclusion for analysis (34 FRS, 75 SRS). The most common indication for stent insertion included sepsis (79%). On multivariate analysis, both acute kidney injury as primary indication for stent insertion (odds ratio [OR ] 9.16, 95% confidence interval [CI] 1.91-44.00, p=0.006) and UWT (OR 0.34, 95% CI 0.15-0.74, p=0.007) were found to be significantly associated with FRS placement. A receiver operator characteristic curve analysis demonstrates an optimal UWT cutoff of 3.2 mm (sensitivity 60.6%, specificity 83.3%). CONCLUSIONS: Elevated UWT and acute kidney injury as an indication for urgent urinary decompression in the setting of obstructing ureteric stones are predictive of FRS placement. These patients may benefit from upfront nephrostomy tube insertion.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".