Do ureteral stent diameter and length or patient demographics play a role in stent encrustation?
Bibliographic record
Abstract
INTRODUCTION: Retained ureteral stents may constitute a technical challenge. The purpose of this study was to analyze the patient population with retained ureteral stents with regards to stent sizes to better understand if these factors could play a pivotal role in their encrustation. METHODS: After institutional review board approval, we retrospectively analyzed the data of patients who underwent multimodal surgical procedures for the removal of retained ureteral stents from 2010-2018. The primary outcomes analyzed were ureteral stent length and diameter, location of stent placement, and patients' demographics as potential etiologies for encrustation. RESULTS: We included 30 patients with 32 encrusted ureteral stents and 37 patients with 46 forgotten non-retained ureteral stents. Indications for stenting included urolithiasis, malignancy, pregnancy, ureteral stricture, and ureteropelvic junction obstruction. Stent diameters ranged from 6-8.5 Fr. Stent lengths ranged from 22-30 cm, and multilength stents were used too. Smaller diameter stents were less likely to be retained when compared to larger diameter stents (>6 Fr) (p=0.002). Overall stent length was not found to be significant (p=0.251); however, the difference in stent surface area differed by over 1 cm (p<0.001). Patients who were uninsured were more likely to have retained stents (p=0.003). Patients who reside with longer commuting distance to the main academic medical center were more likely to have retained stents (p=0.010). CONCLUSIONS: Retained ureteral stents could be avoided. Taking into consideration ureteral anatomical variation among patients, smaller diameter stents and smaller surface area may prevent encrustation. Uninsured patients with farther distance to seek medical care and females are the most at risk.
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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 | 0.011 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".