Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize the evidence, now extensive, that efforts to reduce prostate cancer mortality by screening and early detection result in overdiagnosis of disease that is clinically insignificant, and would never have been diagnosed in the patient's lifetime in the absence of screening. Overdiagnosis may result in overtreatment, which in the case of prostate cancer often carries significant, long-term quality-of-life effects. The review also addresses the solutions to the problem of overdiagnosis and overtreatment, and summarizes the outcomes of these approaches. RECENT FINDINGS: Screening for prostate cancer has been demonstrated to reduce mortality, although with a high number needed to treat. One approach to this problem is to offer patients with favorable risk disease an initial conservative approach, with close monitoring and treatment for those patients who are reclassified as higher risk over time. Much preclinical data indicates that Gleason 6 prostate cancer does not carry the hallmarks of malignancy. However, a number of recent studies have demonstrated that in patients diagnosed with favorable risk prostate cancer (Gleason 6 or less, prostate-specific antigen <10), about 30% will harbor higher grade cancer and benefit from treatment. These patients are identifiable by a combination of repeat biopsy, serial prostate-specific antigen, and in borderline cases, multiparametric MRI. SUMMARY: Active surveillance is a powerful solution to the problem of overdiagnosis and overtreatment associated with screening for prostate cancer. For the 40-50% of patients with favorable risk prostate cancer, it offers the benefit of personalized medicine, avoiding treatment and related quality-of-life effects altogether in the majority, and providing definitive management for the minority who are reclassified with higher risk disease over time.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".