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Record W4380883292 · doi:10.1002/alz.060621

Poor oral health predicts cognitive impairment in elders

2023· article· en· W4380883292 on OpenAlexaboutno aff
Jung‐Tsu Chen, Stephanie Di‐Shan Tsai, Ching‐An Tseng, Jeng‐Min Chiou, Ta‐Fu Chen, Yen‐Ching Chen, Jen‐Hau Chen

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaCognitionMedicineTooth lossMemory spanPeriodontitisTooth brushingCognitive testVerbal fluency testGerontologyDentistryDiseasePsychiatryWorking memoryNeuropsychologyOral healthPathology

Abstract

fetched live from OpenAlex

Abstract Background Tooth loss, periodontal disease, dementia, and Alzheimer’s disease share several risk factors in older adults. Several previous studies have reported positive associations between these dental problems and global cognitive defects. However, limited is known about the relationships among multiple dental/periodontal statuses and different cognitive domains. Method This six‐year (2011‐2017) cohort study includes 515 older adults (65+). Global cognition was assessed by Montreal cognitive assessment‐Taiwan version (MoCA‐T). For cognitive domains, we used logical memory tests (immediate and delayed theme and free recall) in Wechsler Memory Scale‐Third Edition (WMS‐III) to assess memory. Trail Making Test A and B was used to assess attention and executive function, respectively. Digit span‐forward and backward was used to evaluate attention and working memory/short‐term memory, respectively. Verbal fluency tests were used to assess category/semantic fluency. A Z‐score was calculated for each cognitive domain‐specific test according to baseline mean and standard deviation. Oral health status includes tooth defect (dental caries, tooth wear, cervical abrasion, retained root or crown fracture), periodontal health (disease severity from healthy periodontal status, gingivitis to periodontitis), and arch integrity (no tooth loss nor retained root or crown fracture). Generalized linear mixed models were used to assess the associations above adjust important covariates. Result At baseline, periodontitis was associated with poor performance in executive function [Trail Making Test A: b = ‐0.14, 95% confidence interval (CI) = ‐0.27 to ‐0.01]. The association between baseline severity of tooth defect and poor memory decreased (logical memory‐immediate theme recall: b = ‐0.05, 95% CI = ‐0.10 to ‐0.01); similarly finding was found for attention domain (digit span‐forward: b = ‐0.04, 95% CI = ‐0.07 to ‐0.001). At baseline, loss of arch integrity was associated with a better working memory/short‐term memory (digit span‐backward: β = 0.18, 95% CI = 0.01 to 0.36); as follow‐up time increased, this association decreased over six years (β = ‐0.04, 95% CI = ‐0.07 to ‐0.003). Conclusion Periodontitis, tooth defects, or arch integrity was associated with poor performance of cognitive domains (memory, attention, and executive function) over time. A oral health evaluation may serve as predictors of the preclinical phase of dementia in its upcoming years.

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 score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.345
Teacher spread0.303 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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