Can non‐malignant biopsy features identify men at increased risk of biopsy‐detectable prostate cancer at re‐screening after 4 years?
Bibliographic record
Abstract
OBJECTIVES: To identify pathological features in non-malignant sextant prostate needle biopsies and assess their predictive value for detecting prostate cancer on biopsy 4 years later. PATIENTS AND METHODS: We selected and reviewed the biopsy specimens of 121 men that were diagnosed as non-malignant during the first screening round of the European Randomized Study of Screening for Prostate Cancer (ERSPC), Rotterdam section. Of these 61 (50.4%) were positive for cancer during the second round (the result of a matched random sample). The biopsies were indicated by prostate-specific antigen levels of >or= 3.0 ng/mL. Specimens were scored for high-grade prostatic intraepithelial neoplasia (HGPIN), active and chronic inflammation, biopsy core length and glandular core length. The predictive value of the pathological features for detecting prostate cancer after 4 years was assessed. RESULTS: In the first-round biopsies the incidence of HGPIN was 7.1%; there was active inflammation in 22.4% and chronic inflammation in 51.0%. The mean core length was 9.3 mm and mean glandular core length 7.4 mm; the mean total biopsy length (sum of core lengths) was 56.3 mm and mean total glandular length (sum of glandular core lengths) was 44.6 mm. None of the pathological features in the initial round was significantly related to the detection of cancer in the second round. CONCLUSIONS: In this study of non-malignant prostate biopsy specimens from a screened population, no pathological features could be identified that were predictive for detecting prostate cancer on biopsy 4 years later.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".