Problems and solutions of stent biofilm and encrustations: A review of literature
Bibliographic record
Abstract
A ureteral stent is a commonly implanted urological device in patients with urinary tract obstruction. The main role of these stents is to allow adequate drainage of urine from the kidney into the bladder. Individuals with strictures, tumors, or obstructions from urinary stones do not have adequate urine flow and require ureteral stents as a part of their treatment to avoid potential hydronephrosis and renal failure. Although ureteral stents are highly effective in treating urinary tract obstructions, they have associated morbidities, such as biofilm formation and encrustation. Researchers have studied about how to diminish these negative outcomes by developing novel stent materials. Different coatings and biomaterials have been developed to reduce bacterial adhesion and crystal deposition onto the stent surfaces. Moreover, new investigation technologies, such as microfluidic platforms and encrustation sensors, have been utilized to better study the stents. Biofilms and encrustations can stem from bacterial origins; therefore, understanding the urinary microbiome will also provide insight into the solutions for treating them. There are still some gaps in our knowledge regarding the exact underlying mechanisms of stent-associated biofilms and encrustation. Future studies should include continuous testing of novel stent biomaterials for safety and efficacy, developing new technologies for identifying and extracting biofilms, enriching the assessment of stent encrustation, and diving deeper into understanding the urinary microbiome.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".