Lifestyle behaviors, obesity, and perceived health among men with and without a diagnosis of prostate cancer: A population-based, cross-sectional study
Bibliographic record
Abstract
BACKGROUND: A better understanding of how prostate cancer survivors differ from men without prostate cancer and whether these potential differences vary across demographic subgroups will help to focus and prioritize future public health interventions for improving the health and well-being of prostate cancer survivors. Therefore, our study aims were to compare lifestyle behaviors, body mass index (BMI), and perceived health in men with and without a diagnosis of prostate cancer in a national, population-based sample and to explore whether these comparisons differ for demographic subgroups. METHODS: In a cross-sectional study, men aged > or = 40 were identified from the Behavioral Risk Factor Surveillance System (BRFSS) 2002 data (n = 63,662). Respondents reporting history of prostate cancer (n = 2,524) were compared with non prostate cancer controls (n = 61,138) with regard to daily fruit and vegetable servings (FVPD), smoking, alcohol, sedentary behavior, BMI, and perceived health. Multivariable logistic regression calculated adjusted odds ratios (OR) and 95% confidence intervals (CI) for the entire sample and for age, race, education, and urbanicity subgroups. RESULTS: Men with prostate cancer did not differ from men without prostate cancer with regard to smoking, alcohol, sedentary behavior, and obesity but were more likely to consume >/= 5 FVPD (OR, 95% CI: 1.30, 1.09-1.56) and report poor or fair health (OR, 95% CI: 1.62, 1.33-1.97). Subgroup analyses demonstrated attenuation of the higher likelihood of > or = 5 FVPD among prostate cancer survivors in rural respondents (OR, 95% CI: 0.98, 0.72-1.33). Poorer perceived health was greatest if < or = 65 years of age (OR, 95% CI: 2.54, 1.79-3.60) and nonsignificant if black (OR, 95% CI: 1.41, 0.70-2.82). Smoking and alcohol which were not significant for the sample as a whole, demonstrated significant associations in certain subgroups. CONCLUSION: Although efforts to enhance perceived health and healthy lifestyle behaviors among prostate cancer survivors are warranted, demographic subgroups such as prostate cancer survivors < or = 65 and rural populations may require more aggressive interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".