Feasibility and development of a cariogenic diet scale for epidemiological research
Bibliographic record
Abstract
BACKGROUND: Diet cariogenicity plays a major role as both a protective and risk factor in the development of early childhood caries (ECC). AIM: Develop a scale measuring the cariogenicity of foods and beverages and employ it to describe the cariogenicity of young children's diets and predict dental caries outcomes. DESIGN: Scores of cariogenicity and consumption frequency were applied to food frequency questionnaire (FFQ) collected from an Australian children's cohort study with three time-points of data. One-way ANOVA, with post hoc Tukey test compared mean cariogenic scale measured at 18 months between the subsample of children with caries classification at age 5 years. RESULTS: At 6 months, children's mean cariogenic score was 10.05, increasing to 34.18 at 12 and 50.00 at 18 months. Mean cariogenic scale score at 18 months was significantly higher in children with advanced disease at 5 years (mean scale score: 59.0 ± 15.9) compared to those that were healthy (mean score 47.7 ± 17.5, P = 0.007) or had mild-moderate disease (mean score 48.2 ± 17.3, P = 0.008). CONCLUSIONS: The cariogenic diet scale provides a useful indication of the increasing cariogenicity of children's diets with age and highlights the incorporation of discretionary choice foods and beverages into the diets of young children much earlier than nutritionally recommended.
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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 | 0.042 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".