Next-generation prostate cancer risk calculator for primary care physicians
Bibliographic record
Abstract
INTRODUCTION: Current prostate cancer risk calculators are limited in impact because only a probability of having prostate cancer is provided. We developed the next generation of prostate cancer risk calculator that incorporates life expectancy in order to better evaluate prostate cancer risk in context to a patient's age and comorbidity. METHODS: We combined two cohorts to develop the new risk calculator. The first was 5638 subjects who all underwent a prostate biopsy for prostate cancer detection. The second was 979 men diagnosed with prostate cancer with long-term survival data. Two regression models were used to create multivariable nomograms and an online prostate cancer risk calculator was developed. RESULTS: Of the 5638 patients who underwent a prostate biopsy, 629 (11%) were diagnosed with aggressive prostate cancer (Gleason Score 7[4+3] or more). Of the 979 patients who underwent treatment for prostate cancer, the 10-year overall survival (OS) was 49.6% (95% confidence interval [CI] 46.6-52.9). The first multivariable nomogram for cancer risk had a concordance index of 0.74 (95% CI 0.72, 0.76), and the second nomogram to predict survival had a concordance index of 0.71 (95% CI 0.69-0.72). The next-generation prostate cancer risk calculator was developed online and is available at: http://riskcalc.org/ProstateCA_Screen_Tool. CONCLUSIONS: We have developed the next-generation prostate cancer risk calculator that incorporates a patient's life expectancy based on age and comorbidity. This approach will better evaluate prostate cancer risk. Future studies examining other populations will be needed for validation.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.014 | 0.005 |
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".