Tolerance of local anesthetic for transrectal ultrasound-guided prostate biopsy: our experience and a literature review.
Bibliographic record
Abstract
PURPOSE: To determine whether local anesthetic injection or gel reduced pain during transrectal ultrasound-guided prostate biopsies and whether there was significant difference between quadrant and apex-only anesthesia. METHODS: Between September 2001 and May 2002, 240 male patients with elevated prostate-specific antigen and (or) abnormal digital rectal examination were randomized into 1 of 4 groups: 1) transrectal lidocaine gel, 2) quadrant lidocaine injections, 3) apex-only lidocaine injections, or 4) no local anesthetic. Patients scored their pain on a numerical rating scale where 0 indicated no pain and 10 indicated worst pain. We analyzed mean and standard deviations of scores, using a 1-way analysis of variance (ANOVA) and post hoc multiple comparisons with Tukey's honestly significant difference (HSD) studentized range test to determine whether there were significant differences across the groups. RESULTS: There was no significant difference between local anesthetic gel (mean 3.1, SD 1.9) and no anesthetic (mean 3.5, SD 1.9) or between quadrant (mean 1.7, SD 1.7) and apex-only (mean 2.0, SD 1.8) local anesthetic injections. There was significant difference between quadrant injections (mean 1.7, SD 1.7) and no local anesthetic (mean 3.5, SD 1.9) and between apex-only injections (mean 2.0, SD 1.8) and no local anesthetic (mean 3.5, SD 1.9). CONCLUSION: There was significant pain reduction with local anesthetic injections but not with gel, and since there was no significant difference in efficacy between quadrant and apex-only injections, we recommend apex-only local anesthetic injections for transrectal ultrasound--guided prostate biopsies because it simplifies the injection procedure.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".