Cognitive dysfunction in young subjects with periodontal disease
Bibliographic record
Abstract
BACKGROUND: Periodontal disease is an inflammatory, dysbiotic condition. Studies have shown that in the elderly, periodontal disease was associated with cognitive dysfunction and Alzheimer's disease. OBJECTIVE: To investigate whether young healthy subjects with periodontal disease have lower cognition compared to those without periodontal disease. The salivary cytokines (IL-1β, TNF-α) levels in relation to cognition were also tested. METHODS: In a monocenter, cross-sectional study, forty subjects [mean age (SD) = 34 (5) and 48% female] from western Romania were classified into periodontal disease conditions using radiographic assessment: 10 subjects had aggressive periodontitis (AGG_P), 20 chronic mild-moderate periodontitis (CR_P), and 10 no periodontitis (NL_P). Neuropsychological assessment performed by standardized neurologists and psychologist included Rey Auditory Verbal Learning Test (RAVLT), Montreal Cognitive Assessment test (MOCA), Mini-Mental State Examination (MMSE), and Prague tests. Salivary cytokines levels were determined by ELISA. RESULTS: RAVLT and MOCA delayed recall scores were lower in AGG_P group compared to NL_P and CR_P. The learning curve was also different with subjects with AGG_P showing reduced learning performance. Contrary to our hypothesis, salivary IL-1β associated with immediate but not delayed cognitive scores. CONCLUSIONS: These results showed for the first time that subjects with AGG_P had cognitive dysfunction and IL-1β may play a role in this process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".