Prostate cancer risk group is associated with other-cause mortality in men with localized prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Informed decision-making in localized prostate cancer must consider the natural history of the disease, risks of treatment, and the competing risks from other causes. Other-cause mortality has often been associated with comorbidity or treatment-related side effects. We aimed to examine the association between prostate cancer aggressiveness and other-cause mortality. METHODS: Using the Surveillance, Epidemiology, and End Results (SEER)18 registries, patients diagnosed with localized prostate cancer between 2004 and 2015 were identified. Patients were categorized into low-, intermediate- and high-risk groups. Vital status, death due to prostate cancer, and death due to other causes were based on death certificate information. Survival analyses were performed to assess the association between prostate cancer risk group and mortality while adjusting for demographic variables, year of diagnosis, and initial therapy. RESULTS: A total of 464 653 patients were identified with a median followup of 5.4 years. Cardiovascular disease was the most common cause of mortality during the study period. Compared to low-risk patients, intermediate- and high-risk patients had a higher risk of mortality from other cancers, cardiovascular disease, and other causes of death regardless of initial treatment. Men who underwent surgery as initial therapy had lower cumulative mortality rates compared to those with radiation as their initial therapy. CONCLUSIONS: Intermediate- and high-risk prostate cancers are associated with higher risk of other-cause mortality. This appears to be independent of treatment type and may not be solely explained by comorbidity status. Further studies controlling for comorbidity and treatment burden should be explored.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".