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Record W2035491861 · doi:10.1080/00016350802572322

Dental health and disease determinants among 35-year-olds in Oslo, Norway

2008· article· en· W2035491861 on OpenAlexaff
Rasa Skudutyte‐Rysstad, Leiv Sandvik, Jolanta Aleksejūnienė, Harald M. Eriksen

Bibliographic record

VenueActa Odontologica Scandinavica · 2008
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineLogistic regressionDentistryDemographyDiseasePopulationEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of the present study was to identify non-biological determinants associated with the number of sound teeth (ST) and presence of decayed surfaces (DS) among 35-year-old Oslo citizens. MATERIAL AND METHODS: Randomly selected participants (n=149, response rate 64%) completed a self-administered questionnaire and were examined clinically and radiographically. Dental caries was registered clinically following World Health Organization (WHO) diagnostic criteria for caries registration, and the findings were combined with radiographic caries recordings. The number of sound teeth and the presence of two or more dentine caries lesions (D(3)S > or = 2) were selected as dependent variables. Associations between selected dependent variables and possible determinants were assessed by linear and logistic regression analyses, taking into account the hierarchical relationships between the independent variables. RESULTS: On average, 35-year-olds had 17.1 (SD=5.6) ST. Half of the participants had no DS and 26% had D(3)S > or = 2. Non-Western region of birth, being single, and having a university education were significantly associated with higher numbers of ST. Low family income, presently a smoker, and irregular dental visits were significantly associated with the presence of dentine caries. CONCLUSIONS: The results of this study indicate that several non-biological determinants operating at different levels are important for health and disease in this adult population.

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 score0.008
Threshold uncertainty score0.893

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 head0.024
GPT teacher head0.303
Teacher spread0.279 · 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

Citations25
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueActa Odontologica ScandinavicaSame topicDental Health and Care UtilizationFrench-language works237,207