Tolerability of Transperineal Prostate Biopsy Under Local Anaesthetic Using Pre-Emptive Over-the-Counter Analgesia: An Interventional Study in Patients with Abnormal Clinical Prostate Findings
Bibliographic record
Abstract
Objective: Transperineal prostate (TP) biopsy is the key diagnostic tool for evaluating prostate cancer and is feasible under local anaesthetic (LA) alone. However, concerns about its tolerability encourage use of a multimodal analgesia approach. Pre-emptive over-the-counter analgesia with LA may provide a simple and low-risk option. The objective of this study was to investigate the effects of over-the-counter analgesia on TP biopsies conducted under LA. Methods: This interventional single-centre study investigated 160 participants who undertook a TP biopsy under LA, with and without pre-emptive analgesia (1 g of paracetamol and 400 mg of ibuprofen). Pain tolerability was measured using a visual analogue scale (VAS) at three procedural points (probe insertion, LA infiltration, and biopsy); an overall average VAS score was subsequently calculated. The abstracted secondary variables include patient details (age, prostate size, and PSA level), biopsy details (number of cores and volume of LA used), and preferability for LA use in future TP biopsies. An inferential statistical analysis was performed using Wilcoxon’s Rank Sum non-parametric test, Pearson’s test of correlation, and Pearson’s Chi-squared test. Results: The groups were comparable in age, prostate size, and PSA level. Median VAS scores were consistently lower in the intervention cohort, but without statistical significance. A higher volume of LA was associated with lower overall VAS (p = 0.03). LA was strongly preferred over GA for hypothetical future TP biopsies in both cohorts. Conclusions: Pre-emptive analgesia does not significantly improve tolerability of TP biopsy under LA. Our study substantiates evidence that TP biopsy is generally well tolerated under LA and preferred over GA.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".