A Novel Prostate Cancer Prevention Strategy: Prevention and Management of Occult Prostatitis
Bibliographic record
Abstract
Prostate cancer is a significant public health concern. Worldwide incidence data from 2020 indicates approximately 1.4 million new cases diagnosed annually, and mortality data indicates over 375,000 annual deaths. Trends indicate increasing incidence and mortality. Clearly, improved prostate cancer prevention, early detection and treatment are needed. Better primary prostate cancer prevention is the most crucial. Occult infections have been implicated in many different chronic diseases. Due to this relationship between chronic disease and chronic infection, the authors have been promoting advanced screening for chronic occult infections using DNA amplification/detection methods such as Polymerase Chain Reaction (PCR) for over 5 years. After conducting over 100 PCR-based infection tests in clinic patients, the authors have observed a very strong correlation between patients with known prostate cancer and the presence of pathogens associated with chronic prostatitis. Published literature confirms that the same relationship has also been noted by others.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".