Comparison of 8, 10, 12, 16, 20 cores prostate biopsies in determination of prostate cancer and importance of prostate volume
Bibliographic record
Abstract
INTRODUCTION: In this study, we evaluate the relationship between increasing core numbers and cancer detection rate. METHODS: We included 1120 patients with prostate-specific antigen levels ≤20 ng/mL and/or suspicious digital rectal examination findings in this study. All patients had a first-time prostate biopsy and 8, 10, 12, 16, and 20 core biopsies were taken and examined in different groups during the study. Multiple logistic regression analysis was made to reach the factor affecting the cancer detection rate between the patients with and without cancer. A p < 0.05 was considered statistically significant. RESULTS: Out of 1120 patients, 221 (19.7%) had prostate cancer. Again of the total 1120 patients, 8 core biopsies were taken from 229 (20.4%); 10 core biopsies from 473 (42.2%); 12 core biopsies from 100 (8.9%); 16 core biopsies from 140 (12.5%); and 20 core biopsies from 178 (15.9%) patients. The increase in the core number increased the cancer detection rate by 1.06 times (p = 0.008). CONCLUSIONS: As long as prostate volume increases, increasing the core number elevates the cancer detection rate. Thus, the rate of missed cancer will be reduced and the rates of unnecessary repetitive biopsy decreases.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".