Needle biopsy size and pathological Gleason Score diagnosis: No evidence for a link
Bibliographic record
Abstract
BACKGROUND: Biopsy Gleason score (GS), in combination with other clinical parameters, is important to take a therapeutic decision for patients with diagnosis of localized prostate cancer. However, preoperative GS is often upgraded after a radical prostatectomy. Increasing the amount of tissue in prostate biopsy may be a way to avoid this issue. We evaluate the influence of a larger biopsy needle size on the concordance between biopsy and pathological GS. METHODS: We analyzed paired biopsies and prostatectomy specimens from 104 cases of men with clinically localized prostate cancer. At the time of prostate biopsy, the patients were prospectively randomized into two needle groups (16-Gauge [G] and 18G) using a 1:1 ratio. GS concordance was estimated performing kappa statistic testing, overall concordance rate and risk to under grade biopsy GS=6. A logistic regression analysis was performed to evaluate the patients' characteristics as possible risk factors. RESULTS: The overall concordance between prostate biopsy and pathological GS was 76.9% and 75.6% (p = 0.875) and the k values were 0.821 and 0.811 (p = 0.424), respectively, for 16G and 18G needle study groups. The risk to undergrade a biopsy GS=6 was 21.1% and 15.4% (p = 0.709) using a 16G and 18G needle, respectively. Age, prostate-specific antigen, prostate volume and needle calibre were not independently associated with a higher risk of GS discordance. CONCLUSIONS: Needle size does not affect the concordance between biopsy and pathological GS. Although GS is not the only way to determine treatment, it is still an unresolved urological issue.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".