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Record W2098923537 · doi:10.1177/0022034509356779

Long-term Dental Visiting Patterns and Adult Oral Health

2010· article· en· W2098923537 on OpenAlexaff
W. Murray Thomson, Sonia Williams, Jonathan M. Broadbent, Richie Poulton, David Locker

Bibliographic record

VenueJournal of Dental Research · 2010
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Toronto
FundersNational Institute of Dental and Craniofacial Research
KeywordsMedicineOral healthAttendanceDentistryCohort studyCohortProspective cohort studyTooth loss

Abstract

fetched live from OpenAlex

To date, the evidence supporting the benefits of dental visiting comes from cross-sectional studies. We investigated whether long-term routine dental visiting was associated with lower experience of dental caries and missing teeth, and better self-rated oral health, by age 32. A prospective cohort study in New Zealand examined 932 participants' use of dentistry at ages 15, 18, 26, and 32. At each age, routine attenders (RAs) were identified as those who (a) usually visited for a check-up, and (b) had made a dental visit during the previous 12 months. Routine attending prevalence fell from 82% at age 15 to 28% by 32. At any given age, routine attenders had better-than-average oral health, fewer had teeth missing due to caries, and they had lower mean DS and DMFS scores. By age 32, routine attenders had better self-reported oral health and less tooth loss and caries. The longer routine attendance was maintained, the stronger the effect. Routine dental attendance is associated with better oral health.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.462
Teacher spread0.400 · 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 source (direct Gemma or distilled Codex), 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

Citations319
Published2010
Admission routes1
Has abstractyes

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