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Record W2037047185 · doi:10.1111/ipd.12023

High caries prevalence and risk factors among young preschool children in an urban community with water fluoridation

2013· article· en· W2037047185 on OpenAlexaff
Catherine Hong, Bagramian Er, S M Hashim Nainar, Lloyd H. Straffon, Liang Shen, Chin‐Ying Stephen Hsu

Bibliographic record

VenueInternational Journal of Paediatric Dentistry · 2013
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineBreastfeedingEthnic groupPopulationDemographyEarly childhood cariesHomogeneousPediatricsOral healthEnvironmental healthDentistry

Abstract

fetched live from OpenAlex

BACKGROUND: Singapore is unique in that it is a 100% urban community with majority of the population living in a homogeneous physical environment. She, however, has diverse ethnicities and cultures as such; there may be caries risk factors that are unique to this population. AIM: The aims were to assess the oral health of preschool children and to identify the associated caries risk factors. DESIGN: An oral examination and a questionnaire were completed for each consenting child-parent pair. RESULTS: One hundred and ninety children (mean age: 36.3 ± 6.9 months) were recruited from six community medical clinics. Ninety-two children (48.4%) were caries active. The mean d123 t and d123 s scores were 2.2 ± 3.3 and 3.0 ± 5.6, respectively. Higher plaque scores were significantly (P < 0.0005) associated with all measures of decay (presence of decay, dt, ds). The risk factors for severity of decay (i.e., dt and ds) include child's age, breastfeeding duration, and parents' ability to withhold cariogenic snacks from their child. CONCLUSIONS: The high caries rate suggests that current preventive methods to reduce caries in Singapore may have reached their maximum effectiveness, and other risk factors such as child's race, and dietary and breastfeeding habits need to be addressed.

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 score0.004
Threshold uncertainty score0.986

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 communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.253
Teacher spread0.246 · 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

Citations34
Published2013
Admission routes1
Has abstractyes

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