Prostate-specific antigen tests and prostate cancer screening: an update for primary care physicians.
Bibliographic record
Abstract
Prostate cancer is a highly prevalent malignancy. Using serum prostatic-specific antigen (PSA) levels to screen for prostate cancer has led to a greater detection of this cancer, at earlier stages. However, screening for prostate cancer by determining PSA levels remains controversial. Concerns include the risk of overdiagnosis and conversely, the failure to detect all prostate cancers. This article, aimed at primary care practitioners, reviews the characteristics of an ideal screening test, in relation to the characteristics of the PSA test. It then discusses the implications of recent findings from two large, randomized, prospective screening trials: the American Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) screening trial and the European Randomized Study of Screening for Prostate Cancer (ERSPC) trial. The latter trial demonstrated a modest survival benefit from PSA screening. Lastly, the article summarizes recommendations from recently updated guidelines about PSA testing from the American Urological Association (AUA), and it discusses when a primary care practitioner might refer a patient to a urologist.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".