A Single Dose of Intraoperative Antibiotics Is Sufficient to Prevent Urinary Tract Infection During Ureteroscopy
Bibliographic record
Abstract
BACKGROUND: American Urology Association (AUA) Best Practice Guidelines for ureteroscopic stone treatment recommend antibiotic coverage for <24 hours following the procedure. The purpose of this study was to evaluate if the addition of postoperative antibiotics reduces urinary tract infections (UTIs) following ureteroscopic stone treatment beyond the recommended preoperative dose. METHODS: A retrospective review was performed of consecutive patients at two institutions, University of British Columbia and Massachusetts General Hospital, Harvard. All patients received a single dose of antibiotics before ureteroscopic stone treatment. A subset of patients was also given postoperative antibiotics. The rate of UTI was compared in patients receiving only preoperative antibiotics (group 1) vs those who received pre- and postoperative antibiotics (group 2). RESULTS: Eighty-one patients underwent ureteroscopy for renal calculi. Mean time to follow up was 42 ± 88 days. Eight (9.9%) patients in total (two from group 1 and six from group 2, p = 0.1457) developed UTIs postoperatively. In group 1, both patients presented with pyelonephritis (n = 2); those patients with infections in group 2 presented with urosepsis (n = 2) and cystitis (n = 2) and two patients had asymptomatic bacteriuria. Risk factors such as preoperative stenting, nephrostomy tubes, and foley catheters neither differed between groups nor did they predispose patients to postoperative infections. CONCLUSIONS: The postoperative UTI rate in this study (9.9%) is consistent with previous reports. Our data suggest that a single preoperative dose of antibiotics is sufficient, and additional postoperative antibiotics do not decrease infection rates after ureteroscopic stone treatment. Risk for selection bias is a potential limitation.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".