How reliable is 12-core prostate biopsy procedure in the detection of prostate cancer?
Bibliographic record
Abstract
INTRODUCTION: Prostate biopsies incur the risk of being false-negative and this risk has not yet been evaluated for 12-core prostate biopsy. We calculated the false-negative rate of 12-core prostate biopsy and determined the patient characteristics which might affect detection rate. METHODS: We included 90 prostate cancer patients (mean age of 64, range: 49-77) diagnosed with transrectal ultrasound guided 12-core prostate biopsy between December 2005 and April 2008. All patients underwent radical retropubic prostatectomy and the 12-core prostate biopsy procedure was repeated on surgical specimen ex-vivo. Results of preoperative and postoperative prostate biopsies were compared. We analyzed the influence of patient age, prostate weight, serum prostate-specific antigen (PSA) level, free/total PSA ratio, PSA density and Gleason score on detection rate. RESULTS: In 67.8% of patients, prostate cancer was detected with repeated ex-vivo biopsies using the same mapping postoperatively. We found an increase in PSA level, PSA density and biopsy Gleason score; patient age, decreases in prostate weight and free/total PSA ratio yielded higher detection rates. All cores, except the left-lateral cores, showed mild-moderate or moderate internal consistency. Preoperative in-vivo biopsy Gleason scores remained the same, decreased and increased in 43.3%, 8.9% and 47.8% of patients, respectively, on final specimen pathology. CONCLUSIONS: The detection rate of prostate cancer with 12-core biopsy in patients (all of whom had prostate cancer) was considerably low. Effectively, repeat biopsies can still be negative despite the patient's reality of having prostate cancer. The detection rate is higher if 12-core biopsies are repeated in younger patients, patients with high PSA levels, PSA density and Gleason scores, in addition in patients with smaller prostates, lower free/total PSA ratios.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".