SIU-ICUD: Screening and Early Detection of Prostate Cancer
Bibliographic record
Abstract
Background/Objectives: Randomised trials show that screening with prostate-specific antigen (PSA) and systematic prostate biopsies can reduce prostate cancer mortality but leads to high rates of overdiagnosis. Today, improved diagnostic methods more selectively detect potentially lethal, high-grade prostate cancer. Methods: This is a narrative review of modern diagnostic methods, ongoing trials, national policies and knowledge gaps related to screening and early detection of prostate cancer. Results: Screening intervals can be prolonged in men with PSA values below around 1 ng/mL as these men are at very low long-term risk of prostate cancer death. Overdiagnosis can be reduced by magnetic resonance imaging (MRI) and lesion-targeted prostate biopsies. Risk calculators and ancillary biomarkers can select men for further investigation and thereby reduce resource needs. These new methods are evaluated in large, randomised screening trials. The remaining knowledge gaps include optimal PSA cut-offs, screening intervals, start and stop ages, and the long-term balance between benefits and harm. Until recently, almost no national healthcare authority recommended population-based screening for prostate cancer. Now, the European Union Council recommends an evaluation of the feasibility of organised, risk-stratified screening. This has led to several pilot projects. In some other parts of the world, such as sub-Saharan Africa and the Caribbean, such initiatives are lacking despite high prostate cancer mortality rates. Conclusions: Risk-stratified prostate cancer screening including MRI and targeted biopsy reduces overdiagnosis. Results from ongoing research are needed to optimise screening protocols and to define long-term benefits and harms. Initiatives for early detection and screening are emerging across the world but are still lacking in many countries with high prostate cancer mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".