An International Case-Control Study of Adult Diet and Brain Tumor Risk: A Histology-Specific Analysis by Food Group
Bibliographic record
Abstract
PURPOSE: Existing studies of diet and adult brain tumors have been limited by small numbers in histology-specific subgroups. Dietary data from an international collaborative case-control study on adult brain tumors were used to evaluate associations between histology-specific risk and consumption of specific food groups. METHODS: The study included 1548 cases diagnosed between 1984 and 1991 and 2486 control subjects from 8 study centers in 6 countries. Of the 1548 cases, 1185 were gliomas, 332 were meningiomas, and 31 were other tumor types. Dietary consumption was measured as average grams per day. RESULTS: We found inverse associations between some vegetable groups and glioma risk, the strongest for yellow-orange vegetables (odds ratio [OR], 0.7, 95% confidence interval [CI], 0.5-0.9 for the 4th vs. 1st quartile of consumption, p for trend<0.001), and the association was limited to specific glioma subtypes. There was no association with cured meat. Non-cured meat was associated with a modest increase in glioma risk (OR, 1.3; 95% CI, 1.0-1.7 for 4th quartile vs. 1st quartile, p for trend=0.01). We also found positive associations between egg, grain, and citrus fruit consumption and glioma but not meningioma risk. CONCLUSIONS: Our study suggests that selected dietary food groups may be associated with adult gliomas and its subtypes but not meningiomas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".