Bridging the knowledge gap: past, present and future of antibiotic use for ureteral stents
Bibliographic record
Abstract
OBJECTIVE: To evaluate the available literature on ureteric stent-related infections, the use of antibiotics and bacterial colonisation to identify the current incidence of stent-related infections, unveil knowledge gaps and generate potential hypotheses for future research. METHODS: A literature review was conducted using PubMed, Cochrane and urological association websites identifying relevant English literature published between 1983 and January 2024. RESULTS: There is a worldwide lack of guidelines for antibiotic prophylaxis for stent placement, exchange or extraction. In patients with a negative preoperative urine culture undergoing ureteroscopy and stent placement, it may be considered to only provide prophylaxis in presence of risk factors. However, in pre-stented patients a preoperative urine culture is important to guide prophylaxis during endourological surgery. During stent indwell time, antibiotic prophylaxis does not show any advantage in preventing urinary tract infections (UTIs). There is no strong evidence to support the use of antibiotics at time of stent removal. In the absence of any clear evidence, management strategies for treating UTIs in patients with ureteric stents vary widely. Stent exchange could be considered to remove the biofilm as a potential source of bacteria. Stent culture can help to guide treatment during infection as urine culture and stent culture can differ. CONCLUSION: In terms of good antibiotic stewardship, urologists should be aware that unnecessary use of antibiotics provokes bacterial resistance. There is a great need for further research in the field of antibiotic prophylaxis and stent-related infections to develop evidence that can help shape clear guidelines for this very common urological practice.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".