Estimating the Rate of Overdiagnosis With Prostate Cancer Screening: Evidence From the Finnish Component of The European Randomized Study of Screening for Prostate Cancer
Bibliographic record
Abstract
Abstract Purpose: Screening for prostate cancer may have limited impact on decreasing prostate cancer-related mortality. A major disadvantage is overdiagnosis, whereby lesions are identified that would not have become evident during the man’s lifetime if screening had not taken place. The present study aims to estimate the rate of overdiagnosis using Finnish data from the European randomized trial of prostate screening. Methods: We used data from 80,149 men randomized to a screening or a control group, distinguishing four birth cohorts. We used the “catch-up method” to identify when the difference in the cumulative incidence of prostate cancer between the screening and control groups had stabilised, implying that the screening has no further effect. We define the overdiagnosis rate to be the relative excess cumulative incidence in the screened group at that point. As an independent method, we also examined the diagnosis rates of T1c tumours as an indicator of early tumors detected by PSA. Results: The estimates of overdiagnosis rates from the catch-up method using the full period of available follow-up ranged between cohorts from 2.3% to 15.4%, and the T1c analysis gave very similar results. Conclusions: Some overdiagnosis has occurred, but there is uncertainty about its extent. A long follow-up is required to demonstrate the full impact of screening. We evaluated the overdiagnosis rates at a population level, associated with being offered screening, taking account of contamination (screening among the controls). The overall evaluation of screening should incorporate mortality benefit, cost-effectiveness and quality of life.
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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 | 0.107 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".