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Record W2010708548 · doi:10.1080/01635580802143851

Salivary Gland Cancer: An Exploratory Analysis of Dietary Factors

2008· article· en· W2010708548 on OpenAlexaffabout
Jamie I. Forrest, Peter T. Campbell, Nancy Kreiger, Margaret Sloan

Bibliographic record

VenueNutrition and Cancer · 2008
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsUniversity of TorontoCancer Care OntarioUniversity of Guelph
Fundersnot available
KeywordsMedicineOdds ratioSalivary gland cancerCancerConfidence intervalPopulationFamily historyLogistic regressionCancer registryBody mass indexDemographyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.066
GPT teacher head0.324
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2008
Admission routes2
Has abstractyes

Explore more

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