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Record W1993292085 · doi:10.1155/2013/687139

Relationship between Subjective Oral Health Status and Lifestyle in Elderly People: A Cross-Sectional Study in Japan

2013· article· en· W1993292085 on OpenAlexfundno aff
Masami Yoshioka, Daisuke Hinode, Masaaki Yokoyama, Aiichiro Fujiwara, Yasuhiko Sakaida, Kenji Toyoshima

Bibliographic record

VenueISRN Dentistry · 2013
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
FundersUniversité Laval
KeywordsCross-sectional studyGerontologyOral healthMedicineEnvironmental healthPsychologyDemographyFamily medicineSociology

Abstract

fetched live from OpenAlex

Objective. The aim of this study was to assess the relationship between subjective oral health status and lifestyle in elderly people living in Japan. Methods. Subjects were 5383 inhabitants of the Kagawa Prefecture, Japan, at the age of 75-100. Records of the number of remaining teeth and the data from self-reported questionnaire were analyzed statistically. Results. Remaining teeth were significantly correlated to "no current smoking," while not related to other lifestyle. On the other hand, "subjective masticatory ability" defined as a condition allowing chewing all foods well was related to favorable lifestyles. "Subjective masticatory ability" was also related to "not feeling stress," "no deviated food habit" as well as to other good oral health conditions. A logistic regression analysis for "remaining teeth more than 20" revealed a significant relationship between "no current smoking" (OR = 1.582) and "no alcohol drinking" (OR = 0.851). Regarding "subjective masticatory ability," all favorable lifestyles analyzed in this study were found to be significant positive factors. Conclusions. "Subjective masticatory ability" seems to be strongly associated with favorable lifestyles. Therefore, it can be suggested that "subjective masticatory ability" might be a good landmark for quality of life of elderly people in addition to the number of remaining teeth.

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)0.001
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 score0.230
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.046
GPT teacher head0.378
Teacher spread0.332 · 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

Citations14
Published2013
Admission routes1
Has abstractyes

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