Oral Infections and Orofacial Pain in Alzheimer's Disease: A Case-Control Study
Bibliographic record
Abstract
BACKGROUND: Dental infections are frequent and have recently been implicated as a possible risk factor for Alzheimer's disease (AD). Despite a lack of studies investigating orofacial pain in this patient group, dental conditions are known to be a potential cause of pain and to affect quality of life and disease progression. OBJECTIVES: To evaluate oral status, mandibular function and orofacial pain in patients with mild AD versus healthy subjects matched for age and gender. METHODS: Twenty-nine patients and 30 control subjects were evaluated. The protocol comprised a clinical questionnaire and dental exam, research diagnostic criteria for temporomandibular disorders, the McGill Pain Questionnaire, the decayed, missing, and filled teeth index, and included a full periodontal evaluation. AD signs and symptoms as well as associated factors were evaluated by a trained neurologist. RESULTS: A higher prevalence of orofacial pain (20.7%, p < 0.001), articular abnormalities in temporomandibular joints (p < 0.05), and periodontal infections (p = 0.002) was observed in the study group compared to the control group. CONCLUSION: Orofacial pain and periodontal infections were more frequent in patients with mild AD than in healthy subjects. Orofacial pain screening and dental and oral exams should be routinely performed in AD patients in order to identify pathological conditions that need treatment thus improving quality of life compromised due to dementia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".