Metabolic Alterations, Vascular Disease and Advanced Prostate Cancer: New Players for Metastatic Advanced Prostate Cancer?
Bibliographic record
Abstract
Introduction: Epidemiologic studies have implicated metabolic imbalance in prostate cancer (PCa) aggressiveness, nevertheless no clear consensus has been reached. The aim of the research was to investigate the association of hypertension, hypercholesterolemia and vascular disease in advanced PCa with and without bone metastases. Methods: Retrospective analysis of 66 patients with diagnosis of advanced PCa between 2005 and 2009 was conducted. We examined hypertension, hypercholesterolemia and vascular disease in 25 patients with advanced PCa and bone metastases versus 41 patients with advanced non-metastatic PCa. Men with incomplete data available, history of hormone therapy or chemotherapy, vascular surgery or other anticancer therapies were excluded. Results:Hypertensionwas significantly linked to advanced PCa with bone metastases (OR 4.5, p = 0.01). Hypercholesterolemia also was significantly associated with aggressive metastatic PCa (OR 3.28, p = 0.01). A significant association was noted between metastatic PCa and vascular disease (OR 3.8, p = 0.04). Conclusions:In our study,hypertension, hypercholesterolemia and vascular disease were significantly related to advanced metastatic PCa. Further research should elucidate these relations in larger samples to confirm these associations and to stabilize future prevention strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".