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Record W4285262509 · doi:10.4103/ccd.ccd_873_20

Preventing Early Childhood Caries through Oral Health Promotion and a Basic Package for Oral Care

2022· article· en· W4285262509 on OpenAlexaff
Ramya Shenoy, Violet D’Souza, Animesh Jain, Baranya Shrikrishna Suprabha

Bibliographic record

VenueContemporary Clinical Dentistry · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineEarly childhood cariesOral healthReferralDentistryIntervention (counseling)Family medicineDental careHealth promotionToothpastePediatricsPublic healthNursing

Abstract

fetched live from OpenAlex

Introduction: Untreated caries in mothers is one of the common risk factors for early childhood caries (ECC). Aim: The aim of the study was to investigate the impact of an oral health promotion program on ECC. Methodology: We conducted a pragmatic trial at 12 primary health centers in a rural community of India with 311 pregnant women using fluoride toothpaste, oral health information through pamphlets, and referral to urgent dental care or atraumatic dental treatment as the test intervention. Data were collected through structured interviews at baseline and oral examination of the children at 2 years of age. Results: Of the 311 women who participated, 274 children were followed up with at 2 years of age. ECC was low and comparable in both groups. When compared with the control group, significantly, more children from the intervention group were breastfed for over 6 months of age ( P = 0.012) and consumed less sugar ( P < 0.001). The number of mothers’ decayed teeth ( P = 0.01), children's sweet scores ( P < 0.001), and the age at which brushing commenced for children ( P = 0.04) increased the likelihood of tooth decay in children. Conclusion: The oral health promotion program had some beneficial effects in preventing caries in children when provided to pregnant women.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.117
GPT teacher head0.412
Teacher spread0.295 · 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.

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

Citations6
Published2022
Admission routes1
Has abstractyes

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