Bibliographic record
Abstract
PURPOSE OF REVIEW: We review new therapies and biomaterials designed to reduce ureteral stent symptoms in patients undergoing ureteroscopy. RECENT FINDINGS: Pharmacologically, alpha blockers and antimuscarinics have been shown to have a synergistic effect and be more effective than either medication alone in reducing stent-related symptoms. Prestenting patients prior to ureterosocpy has been shown to be beneficial for patients with renal stones, offering a better stone-free rate and reduced complications, but not for ureteral stones. Stenting after use of a ureteral access sheath reduced complications and unscheduled emergency visits. Nonsteroidal anti-inflammatories have been shown to prevent pain after stent removal. Surveys showed that patients preferred to remove their own stents via dangle strings at home or undergo cystoscopic removal in the operating room with some type of anesthesia. New materials such as gel-based or biodegradable ureteral stents are being developed to deal with stent-related pain, encrustation, and infection. Antirefluxing stents eliminate vesicoureteric reflux in patients during voiding and may reduce symptoms of back and flank pain. Ureteral stents are involved in many procedures in urology and particularly kidney stone treatments. SUMMARY: Advances in materials and medications will help improve the patient experience for those who receive a ureteral stent.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".