Development of a Nutrition Questionnaire for Dental Caries Risk Factors
Bibliographic record
Abstract
Background: Few existing tools quickly identify dietary behaviours related to dental caries. The objectives of this study were to (i) create a patient-generated questionnaire identifying these dietary behaviours, (ii) capture information on these dietary behaviours in two specific populations via questionnaire pilot testing and (iii) determine questionnaire test-retest reliability. Methods: After development, the questionnaire was reviewed by an expert panel. Cognitive interviewing was conducted, followed by pilot testing in a general university campus population (n = 80) and a university dental clinic (n = 10). Retesting was done with the general campus group (n = 53). Results: Most participants reported never receiving dietary advice from professionals regarding caries. Sugary foods were most often consumed as snacks in the evening or afternoon, then breakfast. In total, 41.3% of campus participants consumed high risk items at least a few times per week or more often. Weekly or more frequent consumption of “other” sugary drinks (e.g., iced tea) was common. In total, 77.6% of questionnaire items had a kappa value representing moderate agreement or greater. Conclusions: Dietary behaviours related to caries were common in this pilot study. Given the high prevalence of caries and low occurrence of prior dietary advice for the same, increased preventive efforts may be warranted.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".