Predictors for Retrograde Ureteral Stenting Failure as an Initial Drainage Method for Emergent Complicated Acute Calculus Obstructive Uropathy
Bibliographic record
Abstract
Objective: To define predictors for initial retrograde ureteral stenting (RUS) failure with the need for the percutaneous nephrostomy (PCN) insertion as a drainage method in patients with complicated acute calculus obstructive uropathy. Methods: We undertook a retrospective evaluation of patients who presented with complicated obstructive calculus uropathy (acute renal failure or obstructive pyelonephritis) between January 2016 and January 2020. Patients in whom there was failure to visualize ipsilateral ureteric orifice and those with extrinsic ureteral obstruction were excluded. Patient demographics and radiological data including stone site, hydronephrosis grade, maximum transverse stone diameter, periureteral density (PUD) and pericalcular ureteric thickness (P-CUT) at the maximum transverse stone diameter were assessed using non-contrast computed tomography at the time of admission. Results: The study included 256 patients who were managed initially by RUS trial. Of them, 48 (18.8 %) had RUS failure. The presence of acute pyelonephritis, increased maximum transverse stone diameter ≥ 9.5 mm, P-CUT ≥ 7.5 mm, and PUD at stone level ≥ 17.5 HU were risk factors associated with RUS failure (P = 0.007, 0.002, < 0.001, and < 0.001, respectively). Conclusion: Initial radiological stone and ureteric characteristics, in addition to the clinical diagnosis of obstructive pyelonephritis, can be used to determine PCN insertion as the preferred option over RUS for urinary drainage.
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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 | 0.000 | 0.005 |
| 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 | 0.001 | 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".