A Case-Control Study of Risk Factors for Salivary Gland Cancer in Canada
Bibliographic record
Abstract
Aim. To assess the effect of various lifestyle risk factors on the risk of salivary gland cancer in Canada using data from a population-based case-control study.Methods. Data from a population-based case-control study of 132 incident cases of salivary gland cancer and 3076 population controls were collected through self-administered questionnaire and analysed using unconditional logistic regression.Results. Four or more servings/week of processed meat product was associated with an adjusted odds ratio (OR) and corresponding 95% confidence interval (CI) of 1.62 (1.02–2.58). Nonsignificantly increased ORs were also related to obesity, >7 drinks/week of alcohol consumption, and occupational exposure to radiation. Furthermore, nonsignificantly decreased ORs were found to be associated with high education level (>12 years) ( OR=0.65 ), high consumption of spinach/squash ( OR=0.62 ) and all vegetables/vegetable juices ( OR=0.75 ), and >30 sessions/month of recreational physical activity ( OR=0.78 ).Conclusions. This study suggests positive associations with consumption of processed meat, smoking, obesity, alcohol drinking, and occupational exposure to radiation as well as negative associations with higher education, consumption of spinach/squash, and physical activity, which suggest a role of lifestyle factors in the etiology of salivary gland cancer. However, these findings were based on small number of cases and were nonsignificant. Further larger studies are warranted to confirm our findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".