Citrus Fruit Intake and Pancreatic Cancer Risk
Bibliographic record
Abstract
OBJECTIVES: The purpose of this systematic review was to investigate the association between dietary intake of citrus fruits and pancreatic cancer risk. METHODS: Authors searched electronic databases and the reference lists of publications of studies addressing diet and pancreatic cancer up to December 2007. All of the epidemiological studies that obtained individual data on dietary intake of citrus fruits and presented risk estimates of the association between intake of citrus fruits and risk of pancreatic cancer were identified and included. Using general variance-based methods, study-specific odds ratios (ORs)/relative risk and associated confidence interval (CI)/SE for highest versus lowest intake of citrus fruits level were extracted from each article. RESULTS: Nine articles including 4 case-control studies and 5 cohort studies proved eligible. Overall summary OR using random effect model suggested an inverse association in risk of pancreatic cancer with intake of citrus fruits (summary OR, 0.83; 95% CI, 0.70-0.98) with large heterogeneity across studies (I = 49.9%). CONCLUSIONS: Pooled results from observational studies showed an inverse association between intake of citrus fruits and the risk of pancreatic cancer, although results vary substantially across studies, and the apparent effect is restricted to the weaker study design (case-control studies).
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".