Prostate Cancer Screening with Prostate-Specific Antigen Testing: More Answers or More Confusion?
Bibliographic record
Abstract
Prostate cancer is a leading cause of morbidity and mortality among middle-aged and older men. Of the solid tumors prostate cancer is rather unique in that it presents in 2 distinct forms, a latent form, which grows slowly and poses no threat to the patient’s life, and an aggressive form, which metastasizes quickly and kills the patient. The discovery of prostate-specific antigen (PSA)2 and the demonstration of its utility for early diagnosis and monitoring of prostatic carcinoma have raised hopes that this simple serological test could be invaluable in screening asymptomatic individuals for early prostate cancer diagnosis. The premise is that such early diagnosis may then lead to early therapeutic interventions, which should improve the overall survival of prostate cancer patients. However, PSA screening of asymptomatic individuals has remained controversial during the last 15 years owing to the lack of evidence for improved patient survival. Recently, the results of 2 major randomized clinical trials on the effectiveness of PSA as a screening tool, from both the US and Europe, have been published. These results are not clear cut. For this reason, the controversy surrounding prostate cancer screening will likely continue for years. Below, we examine this issue with 4 authorities in the field.
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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 | 0.045 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".