Life Expectancy in High‐Grade Incidental Prostate Cancer Patients Versus Population‐Based Controls According to Treatment Type
Bibliographic record
Abstract
OBJECTIVE: To quantify the differences in 5-year overall survival (OS) between high-grade (Gleason sum 8-10) incidental prostate cancer (IPCa) patients and age-matched male population-based controls, according to treatment type: no active versus active treatment. MATERIALS AND METHODS: We relied on the Surveillance, Epidemiology, and End Results (SEER) database (2004-2015) to identify not actively treated and actively treated high-grade IPCa patients. For each case, we simulated an age-matched male control (Monte Carlo simulation), relying on Social Security Administration Life Tables (2004-2020) with 5 years of follow-up. Additionally, we relied on Kaplan-Meier plots to display OS for each treatment type. Multivariable Cox regression models were fitted to predict overall mortality (OM). RESULTS: Of 564 high-grade IPCa patients, 345 (61%) were not actively treated versus 219 (39%) were actively treated, either with radical prostatectomy or radiotherapy. Median OS was 3 years for not actively treated high-grade IPCa patients, with OS difference at 5 years follow-up of 27% relative to their age-matched male population-based controls (37% vs. 64%). Median OS was 8 years for actively treated high-grade IPCa patients, with OS difference at 5 years follow-up of 6% relative to their age-matched male population-based controls (68% vs. 74%). In the multivariable Cox regression model, active treatment independently predicted lower OM (hazard ratio = 0.6; 95% confidence interval = 0.4-0.8; p < 0.001). CONCLUSION: Relative to Life Tables' derived age-matched male controls, not actively treated high-grade IPCa patients exhibit drastically worse OS than their actively treated counterparts. These observations may encourage clinicians to consider active treatment in newly diagnosed high-grade IPCa patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".