Screening Asymptomatic Men for Prostate Cancer Using Prostate-Specific Antigen as An Early Detection Tool: A Review of Existing Literature
Bibliographic record
Abstract
Prostate cancer remains the most common non-skin cancer in men. Prostate-specific antigen (PSA) is recognized as a biomarker for the diagnosis, monitoring, and risk prediction of prostate cancer. However, its role in prostate cancer screening has been controversial. While some authorities have recommended its use for screening, others have stated otherwise. Some clarity is required about its precise role in clinical practice. There need to be more consistent recommendations surrounding using PSA screening in clinical practice. Serum PSA measurements show variable reliability when screening for Prostate cancer, given the dynamics of PSA physiology and the conflicting results from two large, randomized control trials that sought to determine its role in prostate cancer screening and early detection. Hence surrogate measures like PSA density, PSA velocity, free-to-complexed PSA ratio, and percentage Pro-PSA among others, have been used to improve the predictive utility of this assay for Prostate cancer diagnosis. However, the debate on screening still lingers. The current review aims to highlight the controversies and objectively outline the current recommendations. This literature review examined scholarly papers and recommendations about the use of PSA for prostate cancer screening with the aim to rationalize the pros and cons of such approaches. We concluded that although more recent guidelines from the USPSTF recommend that screening be based on individual preference and professional judgment by the healthcare provider, differences in the specific details on how to best employ a PSA screening program still exist and require further review.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".