Prostate-Specific Antigen (PSA)-Based Population Screening for Prostate Cancer: An Evidence-Based Analysis.
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PC) is the most commonly diagnosed non-cutaneous cancer in men and their second or third leading cause of cancer death. Prostate-specific antigen (PSA) testing for PC has been in common practice for more than 20 years. OBJECTIVES: A systematic review of the scientific literature was conducted to determine the effectiveness of PSA-based population screening programs for PC to inform policy decisions in a publicly funded health care system. DATA SOURCES: A systematic review of bibliographic databases was performed for systematic reviews or randomized controlled trials (RCT) of PSA-based population screening programs for PC. REVIEW METHODS: A broad search strategy was employed to identify studies reporting on key outcomes of PC mortality and all-cause mortality. RESULTS: The search identified 5 systematic reviews and 6 RCTs. None of the systematic reviews found a statistically significant reduction in relative risk (RR) of PC mortality or overall mortality with PSA-based screening. PC mortality reductions were found to vary by country, by screening program, and by age of men at study entry. The European Randomized Study of Screening for Prostate Cancer found a statistically significant reduction in RR in PC mortality at 11-year follow-up (0.79; 95% CI, 0.67-0.92), although the absolute risk reduction was small (1.0/10,000 person-years). However, the primary treatment for PCs differed significantly between countries and between trial arms. The American Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) found a statistically non-significant increase in RR for PC mortality with 13-year follow-up (1.09; 95% CI, 0.87-1.36). The degree of opportunistic screening in the control arm of the PLCO trial, however, was high. None of the RCTs found a reduction in all-cause mortality and all found a statistically significant increase in the detection of mainly low-risk, organ-confined PCs in the screening arm. CONCLUSIONS: There was no evidence of a PC mortality reduction in the American PLCO trial, which investigated a screening program in a setting where opportunistic screening was already common practice. Given that opportunistic PSA screening practices in Canada are similar, it is unlikely that the introduction of a formal PSA screening program would reduce PC mortality.
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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 | 0.020 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".