Validation of active surveillance criteria for pathologically insignificant prostate cancer in Asian men
Bibliographic record
Abstract
OBJECTIVES: To validate the ability of contemporary active surveillance protocols to predict pathologically insignificant prostate cancer among Asian men undergoing radical prostatectomy. METHODS: We retrospectively reviewed data on 132 patients eligible for any active surveillance criteria out of 450 patients that underwent radical prostatectomy at several institutions between 2006 and 2013. We validated the ability of seven contemporary active surveillance protocols to predict pathologically insignificant prostate cancer. Traditional and updated criteria to define pathologically insignificant prostate cancer were used. Predictive factors for pathologically insignificant prostate cancer were determined by logistic regression analysis. RESULTS: The predictive rate for updated pathologically insignificant prostate cancer of respective active surveillance criteria was 51% for Johns Hopkins Medical Institution, 41% for Prostate Cancer Research International: Active Surveillance Study, 39% for University of Miami, 32% for University of California, San Francisco, 32% for Memorial Sloan-Kettering Cancer Center, 31% for Kakehi and 27% for University of Toronto. Predictive rates for pathologically insignificant prostate cancer in Asian men were far lower than in USA men. On multivariate analysis, predictive factors of updated pathologically insignificant cancer was prostate volume (odds ratio 1.07, P = 0.004). By adding prostate volume to Prostate Cancer Research International: Active Surveillance Study criteria, the predictive rate for updated insignificant prostate cancer was improved up to 66.7%. CONCLUSIONS: Active surveillance can be carried out considering the clinical characteristics of prostate cancers depending on ethnicity, as current active surveillance criteria seem to have a lower predictive ability value of insignificant prostate cancer in Asian men compared with men in Western countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".