Antimicrobial Prophylaxis in Robot-Assisted Laparoscopic Radical Prostatectomy: A Systematic Review
Bibliographic record
Abstract
It remains unclear whether antibiotic prophylaxis (AP) should be recommended or discouraged in robot-assisted laparoscopic radical prostatectomy (RALP) for prostate cancer (PCa). The development of microbial resistance and side effects are risks of antibiotic use. This systematic review (SR) investigates the evidence base for AP in RALP. A systematic literature search was conducted until 12 January 2023, using Embase, MEDLINE, Cochrane CENTRAL, Cochrane CDSR (via Ovid) and CINAHL for studies reporting the effect of AP on postoperative infectious complications in RALP. Of 436 screened publications, 8 studies comprising 6378 RALP procedures met the inclusion criteria. There was no evidence of a difference in the rate and severity of infective complications within 30 days after RALP surgery between different AP protocols. No studies omitted AP. For patients who received AP, the overall occurrence of postoperative infectious complications varied between 0.6% and 6.6%. The reported urinary tract infection (UTI) rates varied from 0.16% (4/2500) to 8.9% (15/169). Wound infections were reported in 0.46% (4/865) to 1.12% (1/89). Sepsis/bacteraemia and hyperpyrexia were registered in 0.1% (1/1084) and 1.6% (5/317), respectively. Infected lymphoceles (iLC) rates were 0.9% (3 of 317) in a RALP cohort that included 88.6% pelvic lymph node dissections (PLND), and 3% (26 of 865) in a RALP cohort where all patients underwent PLND. Our findings underscore that AP is being administered in RALP procedures without scientifically proven evidence. Prospective studies that apply consistent and uniform criteria for measuring infectious complications and antibiotic-related side effects are needed to ensure the comparability of results and guidance on AP in RALP.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".