Statin use, high cholesterol and prostate cancer progression; results from HCaP‐NC
Bibliographic record
Abstract
BACKGROUND: Statin use is associated with lower advanced prostate cancer risk and reduced prostate cancer-specific mortality, but prior studies were conducted mainly in white men. We examined the effect of statin use on risk of prostate cancer progression in a population-based, minority-enriched cohort. METHODS: We used data from prostate cancer cases (45% African American) diagnosed between 2004 and 2007 who participated in the Health Care Access and Prostate Cancer Treatment in North Carolina cohort (HCaP-NC). We abstracted statin use at diagnosis. Men reported if they had ever been diagnosed with high cholesterol. Multivariable Cox proportional hazards analysis was used to examine associations between statin use and risk of prostate cancer progression (biochemical recurrence or secondary treatment), overall and by race. In secondary analysis, we examined the association between high cholesterol and risk of progression, overall, and by statin use. RESULTS: Of 669 men, 244 (36%) were statin users at diagnosis. During 3.8 years median follow-up, 138 men experienced prostate cancer progression. There was no association between statin use and risk of progression, either overall (HR 1.03; 95%CI 0.72-1.46) or stratified by race. High cholesterol was inversely associated with risk of progression, particularly among statin users (HR 0.43; 95%CI 0.20-0.94; p-interaction = 0.22) and in men with higher perceived access to care (HR 0.57; 95%CI 0.36-0.90; p-interaction = 0.03). Study limitations included a relatively small sample size, short follow-up, and lack of data regarding post diagnosis statin use. CONCLUSIONS: Statin use at diagnosis was not associated with prostate cancer progression in the population-based, minority-enriched HCaP-NC. Greater healthcare engagement, including actively controlling serum cholesterol, may be linked to better prostate cancer-specific outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".