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Record W4388238709 · doi:10.2196/48558

A Silver Fluoride Intervention to Improve Oral Health Trajectories of Young Indigenous Australians: Protocol for a Cluster Randomized Controlled Trial

2023· article· en· W4388238709 on OpenAlexvenueno aff
Joanne Hedges, Gustavo Hermes Soares, Yvonne Cadet‐James, Zell Dodd, Sinon Cooney, James Newman, Murthy Mittinty, Sanjeewa Kularatna, Priscilla Larkins, Roman Zwolak, Rachel Roberts, Lisa Jamieson

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsIntervention (counseling)IndigenousMedicineQuality of life (healthcare)Randomized controlled trialCluster randomised controlled trialCluster (spacecraft)Family medicineOral healthGerontologyPhysical therapyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Indigenous Australian children and adolescents experience profound levels of preventable dental disease. The application of silver fluoride (AgF) to active dental caries is a noninvasive alternative to traditional dental treatment approaches. There is particular utility among Indigenous children and young people with dental fear, who may not have access to timely or culturally safe dental service provisions. OBJECTIVE: The aims of this study are to: (1) assess levels of active dental caries among Indigenous children and young people in 6 Australian states and territories; (2) determine if an AgF intervention reduces levels of active disease over 12-24 months; (3) measure the impact of improved oral health on social and emotional well-being (SEWB) and oral health-related quality of life; and (4) calculate the cost-effectiveness of implementing such an initiative. METHODS: The study will use a 2-arm, parallel cluster randomized controlled trial design. Approximately 1140 Indigenous children and youth aged between 2 and 18 years will be recruited. Each state or territory will have 2 clusters. The intervention group will receive the AgF intervention at the start of the study, with the delayed intervention group receiving the AgF intervention 12 months after study commencement. The primary outcome will be the arrest of active carious lesions, with arrested caries defined as nonpenetration by a dental probe. Secondary outcomes will include SEWB, oral health-related quality of life, and dental anxiety, with covariates including dental behaviors (brushing and dental visits). Effectiveness measures for the economic evaluation will include the number of children and young people managed in primary oral health care without the need for specialist referral, changes in SEWB, the numbers and types of treatments provided, and caries increments. RESULTS: Participant recruitment will commence in May 2023. The first results are expected to be submitted for publication 1 year after a 24-month follow-up. CONCLUSIONS: Our findings have the potential to change the way in which active dental disease among Indigenous children and young people can be managed through the inclusion of specifically tailored AgF applications to improve dental health and SEWB delivered by Indigenous health care workers. Desired impacts include cost savings on expensive dental treatments; improved SEWB, nutrition, social, and learning outcomes; and improved quality of life for both children and young people and their caregivers and the broader Indigenous community. The AgF application could be easily implemented into the training program of Indigenous health workers and yield critical information in the management armamentarium of health and well-being recommendations for Australia's First Peoples. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48558.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.060
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.022
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0120.006
Bibliometrics0.0020.003
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0040.002
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0600.007

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 head0.177
GPT teacher head0.571
Teacher spread0.394 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

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
Published2023
Admission routes1
Has abstractyes

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