Association of Recommended and Non-Recommended Food Score and Risk of Bladder Cancer: A Case-Control Study
Bibliographic record
Abstract
Bladder cancer (BC) is the ninth most common cancer in the world. Dietary patterns and diet quality could reduce exposure to carcinogenic factors postulated to increase the risk of BC. The main objective of this study was to investigate the associations of Recommended Food Score (RFS) and Non-Recommended Food Score (n-RFS) with the risk of BC among Iranian adults. = 200) were selected from the same hospital where cases were recruited. Controls were patients with non-neoplastic diseases that are not related to smoking, or long-term diet modification. Dietary intake was assessed by a 168-item Food Frequency Questionnaire (FFQ), which was validated in Iran. Logistic regression tests were used to estimate the relationship between RFS and n-RFS with BC. The risk of BC decreased by 69% (OR = 0.31; 95% CI:0.13-0.71) among participants belonging to the highest compared with the lowest quartile of RFS. After adjusting for age, sex, smoking, and total energy, a significant inverse trend was observed between the risk of BC and quartile of RFS. Regarding the n-RFS, also expressed as quartiles, subjects in the fourth quartile were at 2.7 times higher risk of having BC compared to participants in the first quartile (OR = 2.7; 95%CI: 1.07-6.78). The findings of this study suggested that, adherence to RFS decreased the risk of BC. Additionally, a higher score of n-RFS may lead to an increased risk of BC. These findings could be used to develop evidence-based recommendations for the prevention of BC in Iran.
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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".