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Record W2913859869 · doi:10.1111/ipd.12470

Feasibility and development of a cariogenic diet scale for epidemiological research

2019· article· en· W2913859869 on OpenAlexaff
Emily Amezdroz, Lauren Carpenter, Shae Johnson, Victoria Flood, Stuart G. Dashper, Hanny Calache, Mark Gussy, Elizabeth Waters

Bibliographic record

VenueInternational Journal of Paediatric Dentistry · 2019
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsRichmond Hospital
Fundersnot available
KeywordsMedicineEarly childhood cariesCohortFood frequency questionnaireEpidemiologyScale (ratio)Post-hoc analysisEnvironmental healthDentistryInternal medicineOral health

Abstract

fetched live from OpenAlex

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.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.013
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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)0.0000.000

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 head0.107
GPT teacher head0.433
Teacher spread0.327 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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