Mortality reductions produced by sustained prostate cancer screening have been underestimated
Bibliographic record
Abstract
BACKGROUND: The recently published European Randomized Study of Screening for Prostate Cancer (ERSPC) reported prostate specific antigen (PSA)-based screening to have reduced the prostate cancer death rate by only 20%. However, this is an underestimate caused by (i) including in the 20% the years before the impact of the first screen becomes manifest, and (ii) not having full information for the follow-up years where the effects of the screening are most apparent. This paper provides a re-analysis of the results using time-specific measures, which avoid the first of these sources of error. METHODS: Mortality rate ratios for follow-up years 1-12 were derived from the yearly numbers of prostate cancer deaths and numbers of men being followed in each arm of the ERSPC. To reduce statistical noise, they were based on moving three-year intervals, and a smooth rate ratio curve was fitted to the yearly data, in order to measure the steady state reduction in mortality and to identify the time at which it reached this level. RESULTS: The re-analysis suggests that the sustained reduction in prostate cancer mortality may be more than 50%. CONCLUSION: Re-analysis of the ERSPC data suggests that if screening is carried out for several years, and if follow-up is pursued until the reduction becomes manifest, the reduction in mortality will be 50-60%. An analysis that includes the 2007-2008 follow-up data is required to quantify more precisely the impact of this intervention.
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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 | 0.050 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".