Eliminating biofilm from ureteral stents: the holy grail
Bibliographic record
Abstract
PURPOSE OF REVIEW: Biofilms continue to be a major limiting factor in the long-term use of ureteral stents, promoting the development of chronic infections and antibiotic resistance and encrustation. Apart from stent removal in conjunction with antibiotic therapy, there is currently no treatment proven successful for completely eradicating a biofilm-related infection, highlighting the need for continued research in this area. RECENT FINDINGS: Research continues to focus mainly on biofilm prevention, specifically the development of novel coatings comprising antibacterial, antifouling or bacterial signalling compounds. Notably, all three strategies have generated candidate coatings showing recent success both in vitro and in vivo. SUMMARY: Despite the current lack of a completely biofilm-resistant device, coating or treatment strategy, continued research into the causation of bacterial biofilm formation and the continued development of novel antibacterial, antifouling and antibiofilm compounds is promising. Future work should be aimed at more rigorous testing of candidate coatings from both physical and bacterial challenge standpoints as well as increased in-vivo investigation via clinical trials.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".