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Record W2146058765 · doi:10.2334/josnusd.51.97

Comparison of the effects of secondary prevention in schoolchildren between hospitals with and without mobile dental services in Southern Thailand

2009· article· en· W2146058765 on OpenAlexaff
Sukanya Tianviwat, Stephen Birch, Virasakdi Chongsuvivatwong

Bibliographic record

VenueJournal of Oral Science · 2009
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsMcMaster University
FundersRoyal Golden Jubilee (RGJ) Ph.D. ProgrammeThailand Research Fund
KeywordsMedicineCluster samplingOral healthDentistryLogistic regressionCluster (spacecraft)Secondary preventionFamily medicineEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

The aim of the present study was to compare the performance of hospital clinics with and without adjunct mobile services for the delivery of secondary prevention for caries in Thai schoolchildren. A dental survey was conducted in schools served by different dental services. 711 schoolchildren were selected from primary schools in Southern Thailand by multistage cluster random sampling. WHO basic oral health survey methods were employed to evaluate three outcomes of secondary prevention: 1) Coverage of secondary prevention - all filled teeth (FT+D(F)T) among caries experienced teeth (DMFT), 2) Effectiveness of secondary prevention - successfully filled teeth (FT) among all filled teeth (FT+D(F)T) and 3) Protective effect of secondary prevention- successfully filled teeth (FT) among caries experienced teeth (DMFT). The respective percentages were 74.3, 97.5 and 72.5 in the children served by hospital-only services, and 41.3, 97.2 and 40.2 in the other group. From clustered logistic regression modeling, only the first and third outcomes were significantly different between the two access groups. This study showed that adjunct mobile service may be less effective in secondary prevention.

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 score0.011
Threshold uncertainty score0.147

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 head0.008
GPT teacher head0.324
Teacher spread0.316 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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