Prostate cancer: a serious disease suitable for prevention
Bibliographic record
Abstract
Prostate cancer is among the most common causes of death from cancer in men, and accounts for 10% of all new male cancers worldwide. The diagnosis and treatment of prostate cancer place a substantial physical and emotional burden on patients and their families, and have considerable financial implications for healthcare providers and society. Given that the risk of prostate cancer continues to increase with age, the burden of the disease is likely to increase in line with population life-expectancy. Reducing the risk of prostate cancer has gained increasing coverage in recent years, with proof of principle shown in the Prostate Cancer Prevention Trial with the type 2 5alpha-reductase (5AR) inhibitor, finasteride. The long latency period, high disease prevalence, and significant associated morbidity and mortality make prostate cancer a suitable target for a risk-reduction approach. Several agents are under investigation for reducing the risk of prostate cancer, including selenium/vitamin E and selective oestrogen receptors modulators (e.g. toremifene). In addition, the Reduction by Dutasteride of Prostate Cancer Events trial, involving >8000 men, is evaluating the effect of the dual 5AR inhibitor, dutasteride, on the risk of developing prostate cancer. A successful risk-reduction strategy might decrease the incidence of the disease, as well as the anxiety, cost and morbidity associated with its diagnosis and treatment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".