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Record W2200910550 · doi:10.18357/ijih.102201514140

Orthodontic treatment need of adolescents in the island community of Haida Gwaii, Canada

2015· article· en· W2200910550 on OpenAlexaffvenueabout
Asef Karim, Jolanta Aleksejūnienė, Edwin H. Yen, Mario Brondani, Arminée Kazanjian

Bibliographic record

VenueInternational Journal of Indigenous Health · 2015
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsIconEthnic groupDemographyMedicineSpecialtyCensusGerontologyFamily medicineEnvironmental healthAnthropologyPopulation

Abstract

fetched live from OpenAlex

The aim of this study was to determine the prevalence of malocclusion and orthodontic treatment need according to the Index of Complexity, Outcome, and Need (ICON) among schoolchildren of the island community of Haida Gwaii in northwestern British Columbia, Canada. Two elementary and two high schools in Haida Gwaii were approached for census sampling. Out of 535 schoolchildren, 215 (90 boys and 125 girls) agreed to participate (40.2% response) in this cross-sectional epidemiological study. A trained examiner assessed orthodontic treatment need in children employing the ICON score and the ICON complexity grade. The mean age (N = 215) was 12.9 ± 2.8 years. Of the examined schoolchildren, 67% had Aboriginal ancestry (at least one Aboriginal parent). The mean ICON score (N = 215) was 43.5 ± 26.2. There were no statistically significant differences in ICON scores for gender (t test, p = 0.207), ethnicity (t tests: paternal ethnicity, p = 0.886 and maternal ethnicity, p = 0.389), or school (ANOVA with post hoc Bonferroni adjustment, p = 0.317). Overall, 43.7% of the surveyed Haida Gwaii adolescents needed orthodontic treatment (ICON > 43). Based on the ICON complexity grade, 31% of the schoolchildren had moderate to very difficult malocclusions; therefore, specialty orthodontic services are recommended in this remote community.

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)0.002
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 score0.199
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.054
GPT teacher head0.341
Teacher spread0.287 · 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

Citations2
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Indigenous HealthSame topicOrthodontics and Dentofacial OrthopedicsFrench-language works237,207