Preventing clinical progression and need for treatment in patients on active surveillance for prostate cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Active surveillance is an established treatment option for men with localized, low-risk prostate cancer (CaP). It entails the postponement of immediate therapy with the option of delayed intervention upon disease progression. The rate of clinical progression and need for treatment on active surveillance is approximately 50% over 15 years. The present review summarizes recent data on current methods, attempting to prevent clinical progression. RECENT FINDINGS: Patient selection for active surveillance is the first mandatory step required to lower progression. Adherence to active surveillance protocols is critical in making sure patients are monitored well and treated early when progression occurs. Before active surveillance allocation and during active surveillance follow-up, methods involving multiparametric MRI, prostate specific antigen derivatives, biopsy factors, urinary, tissue and genetic markers can be used to prevent clinical progression and/or identify those at risk for progression. Medications such as 5α-reductase inhibitors and others might inhibit disease progression in patients on active surveillance. SUMMARY: Active surveillance is required because of overdiagnosis, along with our inability to accurately predict individual CaP behavior. Several methods can potentially reduce the risk of CaP progression in patients with active surveillance. However, a measure of uncertainty and fear of progression will always accompany patients with active surveillance and the physicians treating them.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".