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Record W4392754755 · doi:10.1111/scd.12993

The oral health‐related quality of life of children with fetal alcohol spectrum disorder

2024· article· en· W4392754755 on OpenAlexaff
Mohammad Saad Khawer, Keith Da Silva

Bibliographic record

VenueSpecial Care in Dentistry · 2024
Typearticle
Languageen
FieldMedicine
TopicPrenatal Substance Exposure Effects
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFetal Alcohol Spectrum DisorderMedicineOral healthQuality of life (healthcare)Fetal alcoholPediatricsAlcoholEnvironmental healthDentistryPregnancyNursing

Abstract

fetched live from OpenAlex

INTRODUCTION: A child's oral health impacts their development and quality of life. Children who live with fetal alcohol spectrum disorder (FASD) face barriers to dental care combined with poorer oral health outcomes. However, how this affects their oral health-related quality of life (OHRQoL) is largely unknown. Thus, the aim of this study is to examine the OHRQoL of children living with FASD. METHODS: This cross-sectional survey used the Child Oral Health Impact Profile-Short Form-19 (COHIP-SF-19) to evaluate the OHRQoL quality of life of children (aged 8 to 15) living with FASD, compared to healthy controls. RESULTS: A total of 332 children (or their caregivers) completed the survey. The survey results showed that children living with FASD reported significantly more untreated dental conditions. The majority of children in the control group experienced a low impact across the majority of COHIP-SF-19 domains. However, children living with FASD experienced significantly higher impact scores related to oral health, functional well-being, and social-emotional well-being. CONCLUSION: The results demonstrated that children who live with FASD have a poorer OHRQoL. Overall, more investigation is necessary to identify the best ways to improve the OHRQoL of children living with FASD.

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 score0.030
Threshold uncertainty score0.750

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 communication0.0000.000
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 head0.017
GPT teacher head0.312
Teacher spread0.296 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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