Post‐diagnosis alcohol intake and prostate cancer survival: A population‐based cohort study
Bibliographic record
Abstract
Alcohol consumption has been declared a Group 1 carcinogen by the International Agency for Research on Cancer (IARC) and is a potential risk factor for several types of cancer mortality. However, evidence for an association with prostate cancer survival remains inconsistent. We examined how alcohol consumption post-diagnosis was associated with survival after prostate cancer diagnosis. Men diagnosed with prostate cancer (n = 829) in Alberta, Canada between the years 1997 and 2000 were recruited into a population-based case-control study and then followed for up to 19 years for survival outcomes. Pre- and post-diagnosis alcohol consumption, clinical characteristics and lifestyle factors were collected through in-person interviews shortly after diagnosis and again 2-3 years post-diagnosis. Cox proportional hazards were used to examine how post-diagnosis alcohol consumption was associated with all-cause and prostate cancer-specific mortality (competing risk analysis too), in addition to first recurrence/progression or new primary cancer. Most participants reported drinking alcohol (≥once a month for 6 months) post-diagnosis (n = 589, 71.0%). Exceeding Canadian Cancer Society (CCS) alcohol consumption recommendations (≥2 drinks/day) post-diagnosis was associated with prostate cancer-specific mortality relative to non-drinkers (aHR: 1.82, 95% CI: 1.07-3.10) with borderline evidence of a linear trend. Interestingly, those in the highest quartile of drinks/week pre- and post-diagnosis also had a twofold increase for prostate-specific mortality (aHR: 2.67, 95% CI: 1.28-5.56) while controlling for competing risks. Our results support post-diagnosis alcohol consumption was associated with increased mortality after prostate cancer diagnosis, specifically for prostate cancer-related death. Future studies focused on confirming this burden of disease are warranted.
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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) | 0.001 | 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 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".