Bibliographic record
Abstract
PURPOSE OF REVIEW: Ureteral stents are commonly used in urology, but there is no perfect ureteral stent. This review documents developing ureteral technologies and strategies over the past 2 years. This area has some progressive advances in the foreseeable future. RECENT FINDINGS: Publications from 2014 and 2015 from a PubMed search with the words 'ureter' and 'stent' in the title were reviewed. Topics that affected patient symptoms from stents include selecting the proper length of stent, patient education regarding stent symptoms, and how the stent is removed. Stent extraction strings have been studied and not increased the incidence of infection or pain. There have been several publications examining antirefluxing ureteral stents that reduced vesicoureteral reflux during micturition and infection of transplanted kidneys. Other novel methods of removing a stent include new biodegradable ureteral stents and metal beads attached to the stent used in tandem with a magnetic catheter. Several new metal and mesh stents were described for use in patients with malignant ureteral obstruction. Last, new stent coatings with antimicrobial peptides have also been described. SUMMARY: The search continues for the perfect stent and there has been promising progress over the past 2 years.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".