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The life course approach: explaining the association between height and dental caries in Brazilian adolescents

2005· article· en· W2141180448 on OpenAlexaff
Belinda Nicolau, Wagner Marcenes, Paul Allison, Aubrey Sheiham

Bibliographic record

VenueCommunity Dentistry And Oral Epidemiology · 2005
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicinePercentileSocioeconomic statusAnthropometryLogistic regressionDemographyOral healthDentistryEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

AIM: To investigate the relationship between height and dental caries in Brazilian adolescents. METHODS: A cross-sectional survey design was used to collect retrospective data. Of 764 eligible 13-year-old adolescents enrolled in urban private or public schools in a Brazilian town, 652 were clinically examined and interviewed. Data were collected on socioeconomic circumstances, family related variables, oral health behaviour and anthropometric measures (height and weight). Dental caries was measured by decayed, missing and filled teeth (DMFT) index. The DMFT was categorized according to two levels of severity (low DMFT </= 6; high DMFT > 6) using the 75th percentile of the distribution as the cut-off point. Data analysis involved multiple logistic regression. RESULTS: Adolescents who were the second or later child were 1.90 times more likely to have a high DMFT, whilst being a taller adolescent had a protective effect on caries experience (OR = 0.04; 95% CI = 0.00-0.79). In addition, adolescents from rural areas (OR = 2.74; 95% CI = 1.56-4.82), those whose mothers had less than 8 years of education (OR = 2.10; 95% CI = 1.03-4.27) and those who reported high levels of paternal punishment (OR = 1.60; 95% CI = 1.02-2.52) had an increased risk of having a high DMFT. CONCLUSION: There is a relationship between height and dental caries experience in this sample of Brazilian adolescents.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.065
GPT teacher head0.365
Teacher spread0.300 · 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

Citations50
Published2005
Admission routes1
Has abstractyes

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