Antibiotic Utilization Before Endourological Surgery for Urolithiasis: Endourological Society Survey Results
Bibliographic record
Abstract
INTRODUCTION: Ureteroscopy (URS) and percutaneous nephrolithotomy (PCNL) are common procedures performed to treat kidney stones. Despite advances in technology/technique, serious infectious complications can occur. To better understand the preoperative antibiotic administration patterns and adherence to guidelines from the American Urological Association (AUA) and the European Association of Urology (EAU), members of the Endourological Society were sent a clinical vignette-based survey to assess the antibiotic use in a variety of pre-URS and pre-PCNL scenarios. METHODS: Endourological Society members were e-mailed a survey that queried antibiotic therapy duration before uncomplicated URS and PCNL with negative and asymptomatic positive preoperative urine cultures (PUCs). For negative PUC questions, selecting more perioperative antibiotics was considered a "prolonged" course as it extends past the maximum of 24-hour perioperative prophylaxis recommended by both the AUA and EAU. RESULTS: The response rate was 16.3% for a total of 326 responders. Twenty-one percent to 39% of respondents reported giving prolonged courses of preoperative antibiotics before a URS or PCNL with a negative PUC (p < 0.0001). When presented with a negative PUC, more prolonged antibiotic courses were reported with the following hierarchy: PCNL for 2 cm intrarenal stones (39%) > URS for 12 mm renal pelvis stone (28%) > URS for 7 mm distal ureteral stones (21%) (p < 0.0001). In both negative and positive PUC questions, differences were noted in preoperative antibiotic prescribing patterns among site of practice (United States, Canada, Europe, Asia, South America, and Others), type of practice (academic vs all others), years in practice (<10 or ≥10 years), and surgical volume (URS and PCNL >100 or ≤100 annual cases). CONCLUSIONS: Adherence to guidelines on antibiotic administration before PCNL and URS with a negative PUC varies by scenario and provider. In 21% to 39% of negative PUC cases, reported antibiotic use before a URS or PCNL is not consistent with recommendations from the AUA and EAU. Before a positive PUC URS or PCNL, nearly all surveyed provide preoperative antibiotics; however, the regimen length is variable.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".