The impact of marriage on the overall survival of prostate cancer patients: A Surveillance, Epidemiology, and End Results (SEER) analysis
Bibliographic record
Abstract
INTRODUCTION: Marital status has long been associated with positive patient outcomes in several malignances; however, little is known about its influence on prostate cancer. We analyzed data from the Surveillance, Epidemiology, and End Results (SEER) database to evaluate whether married patients with prostate cancer had a better prognosis than unmarried patients. METHODS: We identified 824 554 patients diagnosed with prostate cancer between 1973 and 2012 in the SEER database. Using the Cox proportional hazard models, we analyzed the impact of marital status (single, married, divorced/separated, and widowed) on survival after diagnosis with prostate cancer. Chi-square tests were used to analyze the association between marital status and other variables, and the Kaplan-Meier method was used to estimate survival curves. RESULTS: Married men were more likely to be diagnosed with a lower Gleason score and undergo surgery than patients in the other groups (p<0.001). The married group had a lower risk of mortality caused by prostate cancer than the other groups. The five-year survival rate for married patients was higher than that for patients in the other groups. CONCLUSIONS: Marital status is a prognostic factor for the survival of prostate cancer patients, as being married was associated with better outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".