Bibliographic record
Abstract
Ureteral stents are commonly used in urology to splint an anastomosis or provide drainage of the upper tract. This chapter discusses current stent materials including coatings and drug-elution, the methods used to improve stent symptoms including choosing the correct stent length and potential future technologies including new materials, coatings, and drug-elution. Briefly, there is evidence that stents are not routinely necessary after uncomplicated ureteroscopy. Patients without stents may have significantly less symptoms that those who are stented after ureteroscopy. Infection rates are relatively uncommon and preoperative antibiotics are warranted but there is little evidence for continued antibiotics thereafter. Stent-related biofilms can help create the infection and there are novel coatings and investigations to understand and prevent the development of these biofilms. Almost all patients experience discomfort and stent symptoms. Drug-eluting stents such as ketorolac have been placed into stents to reduce discomfort. There is good evidence that choosing the correct length of stent is most helpful in preventing stent symptoms. Orally administered alpha blockers provide significant reduction in symptoms of the lower tract related to ureteral stents. One day, a new design, possibly coupled with a new biomaterial will help reduce patient symptoms. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.025 |
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".