Characteristics of incidental prostate cancer in the United States
Bibliographic record
Abstract
Abstract Background: Data regarding North-American incidental (cT1a/b) prostate cancer (PCa) patients is scarce. To address this, incidental PCa characteristics (age, PSA values at diagnosis, Gleason score [GS]), subsequent treatment and cancer-specific mortality rates were explored.Methods: Incidental PCa patients were identified within the Surveillance, Epidemiology, and End Results (SEER) database (2004–2015). Descriptive statistics, annual percentage changes (EAPC), Kaplan-Meier plots, as well as Cox regression models were used.Results: Of all 344,031 newly diagnosed non metastatic PCa patients, 5,155 harbored incidental PCa. Annual rates of incidental PCa increased from 1.9% (2004) to 2.5% (2015; p = 0.02). PSA values at diagnosis were 0–4 ng/ml in 48% vs. 4–10 ng/ml in 31% vs. > 10 ng/ml in 21%. Of all incidental PCa patients, 64% harbored GS 6 vs. 25% GS 7 vs. 11% GS ≥ 8. Of all incidental PCa patients, 47% were aged < 70, 35% were between 70–79 and 18% were ≥ 80 years. Subsequently, 71% underwent no local treatment (NLT) vs. 16% radical prostatectomy (RP) vs. 14% radiotherapy (RT). Proportions of patients with NLT increased from 65 to 81% (p = 0.0001) over the study period (2004–2015). CSM-free survival at six years ranged from 58% in GS ≥ 8 patients with NLT to 100% in patients who harbored GS 6 and underwent either RP or RT.Conclusion: Incidental PCa in the United States is rare. Most incidental PCa patients are diagnosed in men aged less than 80 years of age. The majority of incidental PCa patients undergo NLT and enjoy excellent CSM.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".