Salivary Gland Cancer: An Exploratory Analysis of Dietary Factors
Bibliographic record
Abstract
This study was an exploratory analysis of dietary and other risk factors for primary salivary gland cancer in a population-based case-control study in Ontario, Canada. Cases were men and women diagnosed between 1995 and 1996 with a first primary cancer of the salivary gland, identified through the Ontario Cancer Registry. Controls were an age-matched random sample of the population of Ontario, identified through property assessment files. Cases (n = 91) and controls (n = 1897) completed a self-administered questionnaire with information on diet, smoking, height and weight, and other lifestyle and socio-demographic factors. Multivariate logistic regression was used to estimate odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Among dietary variables, high relative to low intakes of alcohol (OR: 1.26; 95% CI: 0.68-2.35), fruits (OR: 1.26; 95% CI: 0.68-2.33), sweets (OR: 1.66; 95% CI: 0.85-3.25), dairy (OR: 1.41; 95% CI: 0.77-2.58), and starchy foods (OR: 1.78; 95% CI: 0.96-3.3) were associated with non-statistically significant increased risk of salivary gland cancer; whereas vegetables and meats were linked with non-statistically significant decreased risks of the disease. Among non-diet factors, male sex, obese BMI, exposure to occupational radiation, family history of cancer, and household income were suggestive of increased disease risk. Future work with larger numbers of cases are needed to further explore these associations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".