Bibliographic record
Abstract
OBJECTIVES: Our objective was to identify food intake patterns that might be associated with the risk of renal cell carcinoma. DESIGN: A total of 461 cases (210 females, 251 males) were age frequency matched to population controls. Diet factors were created using factor analysis of 69 food items from a food-frequency questionnaire. These factors were modelled using logistic regression to identify those associated with renal cell carcinoma. SETTING: We investigated the role of diet in the aetiology of renal cell carcinoma using a population-based case--control study conducted in Ontario between 1995 and 1996. SUBJECTS: Cases were Ontario residents 20 to 74 years of age identified through review of pathology reports in the Ontario Cancer Registry. RESULTS: A 'dessert' diet factor was positively associated with disease for both sexes (odds ratio estimate (OR) for males = 3.7, 95% confidence interval (CI) 2.0-6.9; OR for females = 1.4, 95% CI 0.8-2.2, for the highest vs. lowest quartile). In males, a 'beef' diet factor was identified and was associated with an increased risk of renal cell carcinoma. Furthermore, a 'juices' diet factor also showed an association with increased risk in males (OR = 1.8, 95% CI 1.0-3.1). For females, a positive association was observed between renal cell carcinoma and an 'unhealthy' diet factor (OR = 1.4, 95% CI 0.8-2.4). CONCLUSIONS: Our findings confirmed that high-fat and high-protein diets might be risk factors for renal cell carcinoma. The data also suggest an increased risk associated with juice intake, a finding not previously reported.
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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".