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Record W2115578192 · doi:10.1155/2013/786462

Epidemiology and Risk Factors of Tooth Loss among Iranian Adults: Findings from a Large Community-Based Study

2013· article· en· W2115578192 on OpenAlexaff
Saber Khazaei, Ammar Hassanzadeh Keshteli, Awat Feizi, Omid Savabi, Peyman Adibi

Bibliographic record

VenueBioMed Research International · 2013
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTooth lossAlgorithmMedicineDentistryCross-sectional studyMathematicsOral healthPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: To investigate the prevalence of tooth loss and different prosthetic rehabilitations among Iranian adults, as well as the potential determinants of tooth loss. METHODS: In a cross-sectional community-based study conducted among 8094 Iranian adults living in Isfahan province, a self-administered questionnaire was used to assess epidemiologic features of tooth loss. RESULTS: Thirty-two percent of subjects had all their teeth, 58.6% had lost less than 6, and 7.2% of participants had lost more than 6 teeth. One hundred and sixty-nine individuals (2.2%) were edentulous. Among participants, 2.3% had single jaw removable partial denture, 3.6% had complete removable denture in both jaws, and 4.6% had fixed prosthesis. Others reported no prosthetic rehabilitation (89.5%). In the age subgroup analysis (≤35 and >35 years old) tooth loss was more prevalent among men than women (OR = 2.8 and 1.9, resp., P < 0.01). Also, in both age groups, current and former smokers had higher levels of tooth loss than nonsmokers (P < 0.001 and P < 0.05, resp.). In addition, tooth loss was positively related to metabolic abnormality for age group >35 years (adjusted OR = 1.29, P < 0.01). CONCLUSIONS: Tooth loss is highly prevalent in Iranian adult population. Community programs promoting oral health for prevention of tooth loss should be considered taking into account its major determinants including lower educational level, male gender, smoking, and metabolic abnormality.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.091
GPT teacher head0.423
Teacher spread0.333 · 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

Citations40
Published2013
Admission routes1
Has abstractyes

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