Furthering the prostate cancer screening debate (prostate cancer specific mortality and associated risks)
Bibliographic record
Abstract
Screening for prostate cancer remains a contentious issue. As with other cancer screening programs, a key feature of the debate is verification of cancer-specific mortality reductions. Unfortunately the present evidence, two systematic reviews and six randomized controlled trials, have reported conflicting results. Furthermore, half of the studies are poor quality and the evidence is clouded by key weaknesses, including poor adherence to screening in the intervention arm or high rates of screening in the control arm. In high quality studies of prostate cancer screening (particularly prostate-specific antigen), in which actual compliance was anticipated in the study design, there is good evidence that prostate cancer mortality is reduced. The numbers needed to screen are at least as good as those of mammography for breast cancer and fecal occult blood testing for colorectal cancer. However, the risks associated with prostate cancer screening are considerable and must be weighed against the advantage of reduced cancer-specific mortality. Adverse events include 70% rate of false positives, important risks associated with prostate biopsy, and the serious consequences of prostate cancer treatment. The best evidence demonstrates prostate cancer screening will reduce prostate cancer mortality. It is time for the debate to move beyond this issue, and begin a well-informed discussion on the remaining complex issues associated with prostate cancer screening and appropriate management.
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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 | 0.129 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.028 | 0.041 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".