Bibliographic record
Abstract
Ureteral stents are commonly used in urology. Every urologist is familiar with the problems that are associated with stents including infection, encrustation, and bothersome symptoms. These problems limit and affect the use of ureteral stents which are necessary, even in light of the problems they can cause. New designs such as a helically cut ureteral stent which is designed to stretch and conform to the ureter is designed to improve comfort. Drug-eluting designs with an antimicrobial (triclosan) are designed to reduce bacterial adherence to ureteral stents. Chlorhexidine, an antiseptic, has been incorporated into a stent and held in place by a slow release varnish to prevent biofilm formation. Combinations of antibiotics coated directly on the stent and administered systemically have been shown to reduce stent colonization both in vitro and in vivo. Gel-based ureteral stents were also showed to reduce bacterial infection and colonization. Bioabsorbable materials have also been designed to reduce infection, symptoms and prevent the forgotten stent syndrome. Newer designs including stents based on guidewire technology, gels, and a combination of self-expanding wire stents with polymer films are reviewed. There is hope on the horizon that new stents will be able to effectively tackle problems that are often seen with ureteral stents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".