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Record W2115081882 · doi:10.1590/0004-282x20140140

Evaluation of patients with Alzheimer's disease before and after dental treatment

2014· article· en· W2115081882 on OpenAlexaboutno aff
Thaís de Souza Rolim, Gisele Maria Campos Fabri, Ricardo Nitríni, Renato Anghinah, Manoel Jacobsen Teixeira, José Tadeu Tesseroli de Siqueira, José Augusto Ferrari Cesari, Sílvia Regina Dowgan Tesseroli de Siqueira

Bibliographic record

VenueArquivos de Neuro-Psiquiatria · 2014
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsMedicineOrofacial painDiseaseQuality of life (healthcare)DentistryMcGill Pain QuestionnairePhysical therapyInternal medicineVisual analogue scale

Abstract

fetched live from OpenAlex

Oral infections may play a role in Alzheimer's disease (AD). Objective To describe the orofacial pain, dental characteristics and associated factors in patients with Alzheimer's Disease that underwent dental treatment. Method 29 patients with mild AD diagnosed by a neurologist were included. They fulfilled the Mini Mental State Exam and Pfeffer's questionnaire. A dentist performed a complete evaluation: clinical questionnaire; research diagnostic criteria for temporomandibular disorders; McGill pain questionnaire; oral health impact profile; decayed, missing and filled teeth index; and complete periodontal investigation. The protocol was applied before and after the dental treatment. Periodontal treatments (scaling), extractions and topic nystatin were the most frequent. Results There was a reduction in pain frequency (p=0.014), mandibular functional limitations (p=0.011) and periodontal indexes (p<0.05), and an improvement in quality of life (p=0.009) and functional impairment due to cognitive compromise (p<0.001) after the dental treatment. Orofacial complaints and intensity of pain also diminished. Conclusion The dental treatment contributed to reduce co-morbidities associated with AD and should be routinely included in the assessment of these patients.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.543

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 head0.017
GPT teacher head0.289
Teacher spread0.271 · 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

Citations70
Published2014
Admission routes1
Has abstractyes

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