Increasing rates of NCCN high and very high‐risk prostate cancer versus number of prostate biopsy cores
Bibliographic record
Abstract
BACKGROUND: Recently, an increase in the rates of high-risk prostate cancer (PCa) was reported. We tested whether the rates of and low, intermediate, high and very high-risk PCa changed over time. We also tested whether the number of prostate biopsy cores contributed to changes rates over time. METHODS: Within the Surveillance, Epidemiology and End Results (SEER) database (2010-2015), annual rates of low, intermediate, high-risk according to traditional National Comprehensive Cancer Network (NCCN) and high versus very high-risk PCa according to Johns Hopkins classification were tabulated without and with adjustment for the number of prostate biopsy cores. RESULTS: In 119,574 eligible prostate cancer patients, the rates of NCCN low, intermediate, and high-risk PCa were, respectively, 29.7%, 47.8%, and 22.5%. Of high-risk patients, 39.6% and 60.4% fulfilled high and very high-risk criteria. Without adjustment for number of prostate biopsy cores, the estimated annual percentage changes (EAPC) for low, intermediate, high and very high-risk were respectively -5.5% (32.4%-24.9%, p < .01), +0.5% (47.6%-48.4%, p = .09), +4.1% (8.2%-9.9%, p < .01), and +8.9% (11.8%-16.9%, p < .01), between 2010 and 2015. After adjustment for number of prostate biopsy cores, differences in rates over time disappeared and ranged from 29.8%-29.7% for low risk, 47.9%-47.9% for intermediate risk, 8.9%-9.0% for high-risk, and 13.6%-13.6% for very high-risk PCa (all p > .05). CONCLUSIONS: The rates of high and very high-risk PCa are strongly associated with the number of prostate biopsy cores, that in turn may be driven by broader use magnetic resonance imaging (MRI).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".