Study Protocol for the Culturally Appropriate Recipes for Impactful Nutrition Goals for Communities Study (CARING for Communities)
Bibliographic record
Abstract
that having a greater number of oral health problems will be correlated with a lower BMD as both can occur due to inflammation.One of our models will investigate diet as a covariate.Methods: This analysis will be completed using data from the Canadian Longitudinal Study on Aging (CLSA), a national cohort of over 20,000 men and women ages 50 to 85 years.Hip BMD was quantified using dual-energy x-ray absorptiometry.Oral health problems were assessed with a survey about teeth and gum health, dental visits, and possible food avoidance due to compromised oral health.An oral health score will be assigned based on number of oral health problems reported.Ordinal regression analyses will incorporate risk factors associated with oral health problems and osteoporosis as covariates.Model 1 will include age.Model 2 will add sex, ethnicity, smoking status, and body mass index.Model 3 will add income, physical activity, and diet (categorized as total daily intake of fruit, vegetables, legumes, nuts, fish, dairy, meats, whole grains, calcium fortified foods/beverages, and fibre).This is of interest as maintaining oral health is integral to the ability to maintain a healthy diet as one ages.Results: N/A Conclusions: Findings can inform the use of oral health parameters to identify those at high risk of fracture while also elucidating the relationship with specific food groupings.
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.167 | 0.038 |
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".