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 withother cancer screening programs, a key feature of the debate isverification of cancer-specific mortality reductions. Unfortunatelythe present evidence, two systematic reviews and six randomizedcontrolled trials, have reported conflicting results. Furthermore, halfof the studies are poor quality and the evidence is clouded by keyweaknesses, including poor adherence to screening in the interventionarm or high rates of screening in the control arm. In highquality studies of prostate cancer screening (particularly prostatespecificantigen), in which actual compliance was anticipated inthe study design, there is good evidence that prostate cancer mortalityis reduced. The numbers needed to screen are at least as goodas those of mammography for breast cancer and fecal occult bloodtesting for colo-rectal cancer. However, the risks associated withprostate cancer screening are considerable and must be weighedagainst the advantage of reduced cancer-specific mortality. Adverseevents include 70% rate of false positives, important risks associatedwith prostate biopsy, and the serious consequences of prostatecancer treatment. The best evidence demonstrates prostate cancerscreening will reduce prostate cancer mortality. It is time for thedebate to move beyond this issue, and begin a well-informed discussionon the remaining complex issues associated with prostatecancer 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 | 0.122 | 0.222 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.023 | 0.039 |
| Insufficient payload (model declined to judge) | 0.012 | 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".