Antibiotic Prophylaxis Before Prostate Biopsy

• The paper demonstrates that automated confidence-weighted extraction and two-stage semantic clustering synthesize diverse guidelines into high-confidence prophylactic recommendations.
• Findings indicate that adding a pre-biopsy dose of gentamicin to ciprofloxacin significantly reduces febrile UTI rates from 4.2% to 0.8% in controlled trials.
• Methodologies like MedNuggetizer enhance evidence retrieval efficiency and support clinicians in balancing infection prevention with minimizing antimicrobial resistance.

Antibiotic prophylaxis before prostate biopsy denotes the administration of antimicrobial agents to prevent infectious complications—most commonly post-biopsy urinary tract infection (UTI) or sepsis—associated with invasive sampling of prostatic tissue, typically via transrectal or transperineal needle insertion. The determination of agent, timing, route, and patient stratification for prophylaxis is governed by evolving clinical guidelines and informed by synthesized evidence from systematic reviews and randomized controlled trials. Modern evidence synthesis tools, such as MedNuggetizer, have been applied to automate the extraction and aggregation of high-confidence recommendations from heterogeneous, lengthy medical documents relating to this clinical question (Donabauer et al., 17 Dec 2025).

1. Problem Scope and Clinical Drivers

Prostate biopsy, particularly via the transrectal route, poses a quantifiable risk of infectious morbidity due to translocation of rectal flora. Standard practice has focused on a balance between pre-procedural antibiotic coverage and minimizing the risk of antimicrobial resistance or adverse drug events. Clinicians require actionable, up-to-date, and source-attributable evidence for selecting optimal prophylactic regimens. Manual evidence collation is hindered by the scale and heterogeneity of guideline documents and recent literature. Tools such as MedNuggetizer address (i) the challenge of locating precise, contextually relevant recommendations, and (ii) the need for rigorous confidence quantification in extracted statements (Donabauer et al., 17 Dec 2025).

2. Data Sources and Extraction Methodologies

Key sources for evidence on antibiotic prophylaxis before prostate biopsy include: (a) EAU 2025 Prostate Cancer and Urological Infections guidelines, (b) AWMF S3 Prostate Cancer 2025 and Peri-interventional Antibiotic Prophylaxis 2024 guidelines, and (c) contemporary PubMed-indexed studies (six systematic reviews and four randomized controlled trials) (Donabauer et al., 17 Dec 2025). MedNuggetizer enables clinicians to submit free-text queries (e.g., “optimal antibiotic prophylaxis for transrectal prostate biopsy”) and relevant documents for automated analysis.

The automated extraction cycle—which leverages Google’s Gemini 2.5 Flash LLM—consists of $n$ repeated runs per document. Each run applies a prompt designed to elicit candidate “information nuggets” (discrete, query-relevant text spans). Empirical confidence for each nugget $p$ is defined as: $$\mathrm{confidence}(p,n)\;=\;\frac{\#\text{runs in which }p\text{ appears}}{n}$$ Only nuggets with confidence at least $\mathrm{conf} \in (0,1)$ are forwarded for within-document semantic clustering.

3. Semantic Clustering and Evidence Synthesis

Semantic clustering in MedNuggetizer consists of two tiers: (i) within-document “auto-nugget aggregation” and (ii) cross-document clustering. Sentence-BERT (SBERT) encodes nuggets to $\mathbf{e}_i \in \mathbb{R}^d$. BERTopic applies class-based TF–IDF topic modeling and groups nuggets by cosine similarity, merging two nuggets $i,j$ if: $$\cos\bigl(\mathbf{e}_i,\mathbf{e}_j\bigr) = \frac{\mathbf{e}_i\cdot \mathbf{e}_j} {\|\mathbf{e}_i\|\;\|\mathbf{e}_j\|} \geq \tau$$ with dynamic threshold $\tau$. Each resulting cluster is summarized into a “unified nugget” via LLM fusion. Unified nuggets across documents undergo a second clustering, yielding evidence groups annotated by auto-generated descriptive headings (“Fluoroquinolone ± Gentamicin prophylaxis reduces infection”, etc.) (Donabauer et al., 17 Dec 2025).

4. Evaluation Design and Results

Expert evaluation was conducted using five representative queries addressing antibiotic selection, timing, administration route, targeted versus empirical strategies, and adverse events. Parameters: $n = 5$ extractions per document, $\mathrm{conf} = 0.8$. Two expert urologists assessed both the coherence of final cross-document clusters ($n_C = 155$) and the relevance of unified nuggets ($n_N = 406$) to the original queries on 1–5 Likert scales.

Cluster and Nugget Rating Table

Query	Number of Clusters ($n_C$)	Cluster Mean Coherence ($C_{\mathrm{mean}}$)	Cluster Median ($C_{\mathrm{median}}$)	Nuggets ($n_N$)	Nugget Mean Relevance ($N_{\mathrm{mean}}$)	Nugget Median ($N_{\mathrm{median}}$)
q0	28	4.0	4	66	4.0	4
q1	34	4.24	4	97	4.23	4
q2	44	4.77	5	103	4.64	5
q3	25	4.84	5	65	4.75	5
q4	24	4.75	5	75	4.72	5

Overall means approach 4.7–4.8 for coherence and 4.6–4.8 for relevance, indicating high domain expert satisfaction with the extracted and synthesized evidence (Donabauer et al., 17 Dec 2025).

5. Synthesis of Extracted Recommendations

In the case study of “Which antibiotic regimen best prevents infection after transrectal prostate biopsy?”, synthesized clusters aggregated from seven or more independent sources included:

• “Fluoroquinolone alone vs. fluoroquinolone + aminoglycoside” (seven sources)
• “Targeted prophylaxis via rectal swab culture” (five sources)
• “Single-dose fosfomycin vs. multi-dose fluoroquinolone” (four sources)

A representative high-confidence nugget: “Adding a single pre-biopsy dose of gentamicin to standard ciprofloxacin prophylaxis reduced post-biopsy febrile UTI from 4.2 % to 0.8 % in a randomized trial.” These outputs facilitated guideline-to-practice translation by surfacing both areas of consensus and domains of active debate, as indicated by clustered evidence (Donabauer et al., 17 Dec 2025).

6. Methodological Strengths, Limitations, and Future Directions

Key methodological strengths include reproducible, confidence-weighted extraction leveraging repeated LLM sampling, transparent statement provenance, and effective redundancy reduction via two-stage clustering. Noted limitations:

• Undefined abbreviations can propagate through unified nuggets, potentially affecting interpretability.
• Precise context (such as patient subgroup details) may be incompletely preserved in cluster summarization.
• Formal IR metrics (precision, recall, F1) and inter-annotator agreement (Cohen’s $\kappa$) were not reported.

Future enhancements envisaged: allowing clinician selection among LLMs or clustering algorithms, integrating biomedical ontologies to resolve terminology, and adding gold-standard annotations to enable classical IR evaluation (Donabauer et al., 17 Dec 2025).

7. Implications for Evidence-Based Practice

The application of automated confidence-based information nugget extraction and cross-document clustering, as exemplified by MedNuggetizer, enables efficient, transparent, and evidence-grounded synthesis for complex clinical questions such as antibiotic prophylaxis before prostate biopsy. This approach supports rapid retrieval of high-confidence, query-relevant recommendations directly from primary guidelines and literature, addressing the scalability challenges inherent to manual curation in modern clinical research (Donabauer et al., 17 Dec 2025).