Does screening for prostate cancer reduce prostate cancer mortality?
Bibliographic record
Abstract
Does screening for prostate cancer reduce prostate cancer mortality?To the Editor: Two large randomized controlled studies evaluating the efficacy of prostate cancer screening with prostate-specific antigen (PSA) testing were recently published in the New England Journal of Medicine.These landmark studies received a great deal of media attention and warrant some clarification.One was a US-based study of 76 693 men from the Prostate, Lung, Colorectal and Ovarian (PCLO) Cancer Screening trial, 1 and the other was the European Randomized Study of Screening for Prostate Cancer (ERSPC) of 162 243 men. 2 Subjects were randomly assigned to either PSA screening (in addition to prostate examinations) or no screening (control group).Using cancer-specific mortality as the primary end point, after about 10 years of follow-up, the PLCO study did not find any difference in mortality rates between the screening and control groups.In contrast, the ERSPC study found a 20% reduction in prostate cancer deaths in the screening group, but was associated with a high risk of overdiagnosis.Although the conclusions of these studies appear to be negative or mixed, careful analysis leads to a more positive view.As PSA screening is widespread in Canada, 3 it is important to clarify these issues.In the PCLO study, only 94 prostate cancer-related deaths occurred (50 in the screening group and 44 in the control group, risk ratio 1.13, 95% confidence interval [CI] 0.75-1.70).Critically, there was insufficient power to compare death rates for the followup period.Further, up to 52% of patients in the control group actually underwent PSA testing during the study period.Prostate-specific antigen screening clearly results in earlier diagnosis.Thus, in the control group, a higher proportion of men with more
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".