Physical activity and renal cell carcinoma among black and white Americans: a case-control study
Bibliographic record
Abstract
BACKGROUND: Renal cell carcinoma (RCC) has a higher incidence in blacks than in whites. Physical activity may influence the risk of renal cell cancer, but the evidence is inconsistent. No previous study has investigated this relationship in the black population. METHODS: We examined the association between self-reported physical activity at different ages and risk of RCC in a population based case-control study of 1217 cases (361 black, 856 white) and 1235 controls (523 black, 712 white) frequency-matched on age, race, and gender. Multivariate-adjusted odds ratios (OR) and 95% confidence intervals (CI) were estimated using unconditional logistic regression. RESULTS: Among whites, increased risks of RCC were observed among participants reporting low levels of transportation-related activity in their 20's (OR <1 hr/wk vs >7 hr/wk (95% CI): 1.42 (1.10, 1.83)) and leisure time activity in their 50's (OR <1 hr/wk vs >7 hr/wk (95% CI): 1.49 (1.00, 2.20)). We found no association between physical activity and RCC risk among blacks. CONCLUSION: Our results suggest that physical activity may be inversely associated with RCC risk in whites, but there was no evidence of such an association in blacks. As this is the first study evaluating the effect of physical activity on RCC risk among blacks, further investigations are needed to clarify the relationship in this population.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".