Chronic inflammation is negatively associated with prostate cancer and high-grade prostatic intraepithelial neoplasia on needle biopsy
Bibliographic record
Abstract
Tissue inflammation has been linked to cancer in several disease models. We tested the association between chronic inflammation and prostate cancer (PCa), as well as high-grade prostatic intraepithelial neoplasia (HGPIN), in prostatic needle biopsy specimens. Tissues from 4526 men, who underwent systematic ultrasound-guided sextant needle biopsies of the prostate, were classified in the following order as PCa, or HGPIN, or chronic inflammation or benign. PCa was diagnosed in 1633 (36.1%), HGPIN in 535 (11.8%) and chronic inflammation in 347 (7.7%). Chronic inflammation conferred a protective effect from PCa: odds ratio (OR) = 0.20, 95% confidence interval (CI) = 0.15-0.28. Chronic inflammation was also inversely associated with HGPIN: OR = 0.11, 95% CI = 0.05-0.22. The ORs remained virtually unchanged after adjustment for age, serum prostate-specific antigen (PSA), digital rectal examination (DRE) and gland volume. Chronic inflammation is more frequent in the presence of benign histology than it is in the presence of PCa or HGPIN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".