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Record W2409734469

Does dental care improve the oral health of older adults?

2001· article· en· W2409734469 on OpenAlexaff
David Locker

Bibliographic record

VenuePubMed · 2001
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineOral healthDental careLongitudinal studyDental healthGerontologyFamily medicinePopulationOral examinationEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the relationship between self-perceived change in oral health status and the provision of dental treatment in an older adult population. DESIGN: A longitudinal study with data collection at baseline and after three years. Information on change in oral health was obtained by interviews with study subjects and information on dental treatment over three years was obtained from subjects' dentists. SUBJECTS: Nine hundred and seven subjects took part at baseline and 611 at follow-up. Of the latter, 495 reported at least one dental visit during the three-year observation period and dental treatment information was available for 408. Outcome measures Global transition judgements and change scores derived from four oral health indexes were used to assess change in oral health status. RESULTS: Over the three-year period, one-tenth of subjects reported that their oral health had improved and one-fifth that it had deteriorated. Those who improved made significantly more dental visits and received significantly more dental services that those who deteriorated or did not change (P<0.0001). They also received a broader range of diagnostic, preventive and therapeutic services. The association between change and dental service provision remained after controlling for other potential determinants of oral health. CONCLUSION: The study suggests that improvements in the oral health of older adults depend upon access to comprehensive dental treatments which can address fully their clinical and self-perceived needs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.564

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 head0.012
GPT teacher head0.277
Teacher spread0.265 · 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

Citations66
Published2001
Admission routes1
Has abstractyes

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