Do antibiotics decrease prostate-specific antigen levels and reduce the need for prostate biopsy in type IV prostatitis? A systematic literature review
Bibliographic record
Abstract
INTRODUCTION: Inflammation of the prostate can be a cause of elevated prostate-specific antigen (PSA) in men referred for suspected prostate cancer. This systematic review assesses the evidence for antibiotic therapy in patients with type IV (asymptomatic) prostatitis with regard to reduction of PSA levels and discrimination between prostate cancer and inflammation. METHODS: MEDLINE, EMBASE, and the Cochrane registry were searched for papers reporting on cohorts of men with elevated PSA and type IV prostatitis that were treated with antibiotics. RESULTS: The search yielded 160 papers, of which 11 met the inclusion criteria: two randomized trials and nine cohort studies. In total, the studies reported on 1011 patients with type IV prostatitis, of whom 926 were treated with antibiotics. PSA normalization was seen after antibiotic treatment in 33.2% of patients (95% confidence interval [CI] 24.9-42.8). Meta-analysis of the randomized trials did not demonstrate a higher likelihood of PSA normalization in the antibiotics arm as compared to the control arm (odds ratio [OR] 1.27; 95% CI 0.58-2.76; p=0.553). Four studies performed prostate biopsies in all patients. Although three of these studies demonstrated lower prevalence of prostate cancer in patients in whom PSA had normalized, meta-analysis failed to show a statistically significant difference (OR 0.39; 95% CI 0.06-2.49; p=0.319). CONCLUSIONS: The available evidence does not support antibiotic therapy for differentiation between benign and malignant cause of elevated PSA in men with type IV prostatitis.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".