Active surveillance for good risk prostate cancer: rationale, method, and results.
Bibliographic record
Abstract
BACKGROUND: Many newly diagnosed patients with prostate cancer have "good risk" disease. The challenge is to identify the minority of these patients with aggressive disease and offer them curative treatment, while sparing the remainder the morbidity of unnecessary treatment. PURPOSE: To examine the results of active surveillance with selective delayed intervention in good risk prostate cancer patients. MATERIALS AND METHODS: This was a prospective phase II study of active surveillance of 299 patients. Eighty percent (239 patients) met the criteria for good risk disease: PSA < 10 ng/mL, Gleason < 6, T < 2a. Twenty percent of patients, all of whom who were age 70 or greater, had Gleason 7 cancer or a PSA above 10. RESULTS: At 8 years, overall survival is 85% and disease-specific survival is 99%. A PSA doubling time of < 2 years was linked with likelihood of locally advanced disease. CONCLUSION: Watchful waiting is clearly appropriate for elderly prostate cancer patients with high co-morbidities. For good risk, young, healthy patients, this study supports the feasibility of long-term, close monitoring with early intervention for those who progress rapidly. Approximately two thirds of such patients will remain free of treatment over 8 years.
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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".