Active surveillance in intermediate risk prostate cancer.
Bibliographic record
Abstract
OBJECTIVES: Active Surveillance (AS) has become an established treatment option for men with low-risk prostate cancer (PCa), demonstrating superior functional outcomes and excellent oncologic outcomes.As such, it has been appealing to extend AS to patients with intermediate risk PCa. We provide a review of the current experience with AS in the intermediate-risk PCa population. METHODS: Risk stratification is the key to treatment success. Many clinical factors (age, percent Gleason 4, PSA density, race/ethnicity, and genetic predisposition) and genomic markers have proven prognostic value in the AS population. We performed a systematic review of the currently available data (randomized trials and prospective cohort studies) to establish the status of AS in the intermediate risk patient population. RESULTS: Our ability to predict the natural history of intermediate risk prostate cancer is imperfect. While the benefits of AS make it an appealing option for men with intermediate risk disease, the published experience todate demonstrates that AS for all men with intermediate risk disease leads to higher rates of metastatic disease and loss of the opportunity for cure. These same studiesalso demonstrate that a subset of patients with intermediate risk disease have indolent disease that may benefit from AS. This heterogeneity is not adequately captured with traditional histopathologic staging. Clinical, genomic, and radiologic biomarkers play a key role in appropriate risk stratification and patient selection. The optimal use of these biomarkers in the intermediate riskpatient is currently the subject of intense evaluation. CONCLUSION: Active surveillance for men at the favorable end of intermediate risk prostate cancer is an appealing alternative to radical therapy, but carries a modest but increased risk of metastatic disease compared to low risk cancer. Many biomarkers are currently being evaluated to enhance precise risk stratification of this important subgroup of patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".