Prostate-Specific Antigen (PSA)-Based Population Screening for Prostate Cancer: An Economic Analysis.
Bibliographic record
Abstract
BACKGROUND: The prostate-specific antigen (PSA) blood test has become widely used in Canada to test for prostate cancer (PC), the most common cancer among Canadian men. Data suggest that population-based PSA screening may not improve overall survival. OBJECTIVES: This analysis aimed to review existing economic evaluations of population-based PSA screening, determine current spending on opportunistic PSA screening in Ontario, and estimate the cost of introducing a population-based PSA screening program in the province. METHODS: A systematic literature search was performed to identify economic evaluations of population-based PSA screening strategies published from 1998 to 2013. Studies were assessed for their methodological quality and applicability to the Ontario setting. An original cost analysis was also performed, using data from Ontario administrative sources and from the published literature. One-year costs were estimated for 4 strategies: no screening, current (opportunistic) screening of men aged 40 years and older, current (opportunistic) screening of men aged 50 to 74 years, and population-based screening of men aged 50 to 74 years. The analysis was conducted from the payer perspective. RESULTS: The literature review demonstrated that, overall, population-based PSA screening is costly and cost-ineffective but may be cost-effective in specific populations. Only 1 Canadian study, published 15 years ago, was identified. Approximately $119.2 million is being spent annually on PSA screening of men aged 40 years and older in Ontario, including close to $22 million to screen men younger than 50 and older than 74 years of age (i.e., outside the target age range for a population-based program). A population-based screening program in Ontario would cost approximately $149.4 million in the first year. LIMITATIONS: Estimates were based on the synthesis of data from a variety of sources, requiring several assumptions and causing uncertainty in the results. For example, where Ontario-specific data were unavailable, data from the United States were used. CONCLUSIONS: PSA screening is associated with significant costs to the health care system when the cost of the PSA test itself is considered in addition to the costs of diagnosis, staging, and treatment of screen-detected PCs.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".