Assessing Individual Risk for Prostate Cancer
Bibliographic record
Abstract
PURPOSE: To construct a clinical nomogram instrument to estimate individual risk for having prostate cancer (PC) for patients undergoing prostate specific antigen (PSA) screening, using all risk factors known for PC. PATIENTS AND METHODS: We conducted a cross-sectional study of 3,108 men who underwent a prostate biopsy, including a subset of 408 volunteers with normal PSA levels. Factors including age, family history of PC (FHPC), ethnicity, urinary symptoms, PSA, free:total PSA ratio, and digital rectal examination (DRE) were incorporated in the model. A nomogram was constructed to assess risk for any and high-grade PC (Gleason score >or= 7). RESULTS: Of the 3,108 men, 1,304 (42.0%) were found to have PC. Among the 408 men with a normal PSA (< 4.0 ng/mL), 99 (24.3%) had PC. All risk factors were important predictors for PC by multivariate analysis (P, .01 to .0001). The area under the curve (AUC) for the nomogram in predicting cancer, which included age, ethnicity, FHPC, urinary symptoms, free:total PSA ratio, PSA, and DRE, was 0.74 (95% CI, 0.71 to 0.81) and 0.77 (95% CI, 0.74 to 0.81) for high-grade cancer. This was significantly greater than the AUC that considered using the conventional screening method of PSA and DRE only (0.62; 95% CI, 0.58 to 0.66 for any cancer; 0.69; 95% CI, 0.65 to 0.73 for high-grade cancer). From receiver operating characteristic analysis, risk factors including age, ethnicity, FHPC, symptoms, and free:total PSA ratio contributed significantly more predictive information than PSA and DRE. CONCLUSION: In a PC screening program, it is important to consider age, family history of PC, ethnicity, urinary voiding symptoms, and free:total PSA ratio, in addition to PSA and DRE.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".