ACTIVE SURVEILLANCE WITH SELECTIVE DELAYED INTERVENTION: USING NATURAL HISTORY TO GUIDE TREATMENT IN GOOD RISK PROSTATE CANCER
Bibliographic record
Abstract
PURPOSE: This article reviews the data supporting an approach of active surveillance with selective delayed intervention for good risk localized prostate cancer. The challenge is to identify those patients who are not likely to experience significant progression, while offering radical therapy to those who are at risk. MATERIALS AND METHODS: A prospective phase 2 study of active surveillance with selective delayed intervention was initiated in 1995. Patients were treated initially with surveillance, while those who had a prostate specific antigen (PSA) doubling time (DT) of 2 years or less, or grade progression on re-biopsy were offered radical intervention. The remainder were closely monitored. RESULTS: The cohort consisted of 299 patients with good risk prostate cancer or intermediate risk prostate cancer in men older than 70 years. Median PSA DT was 7.0 years and 35% of the men had a PSA DT of greater than 10 years. The majority of patients remain on surveillance. At 8 years overall actuarial survival was 85% and disease specific survival was 99%. CONCLUSIONS: Most men with favorable risk prostate cancer will die of unrelated causes. The approach of active surveillance with selective delayed intervention based on PSA DT represents a practical compromise between radical therapy in all, which results in overtreatment in patients with indolent disease, and watchful waiting with palliative therapy only, which results in under treatment in those with aggressive disease. Results at 8 years are favorable. Longer followup will be required to confirm the safety of this approach in men with long (greater than 15-year) life expectancy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".