Effects of tooth loss on brain structure: a voxel-based morphometry study
Bibliographic record
Abstract
PURPOSE: One of the most prominent issues in a super-aging society is the rapid increase in dementia patients. Cross-sectional studies in dentistry have indicated that patients with dementia have worse oral health compared to healthy people. The purpose of this study was to clarify the influence of tooth loss on brain structure by comparing the volumes of gray matter (GM) and white matter (WM) between edentulous and dentulous subjects. METHODS: Subjects were recruited from the Denture Clinic at Iwate Medical University Hospital Dental Center. Experiments were performed on edentulous (5 males, 8 females, 81.8±1.24years) and dentulous subjects (4 males, 7 females, 77.1±4.25years). Patients with dementia were excluded from this study. Brain volumes of GM and WM in edentulous and dentulous subjects were compared using intracranial volume, age, gender and history of hypertension as covariates. Analyzed brain areas were identified by transforming the Montreal Neurological Institute coordinate into the anatomical coordinate in edentulous subjects. RESULTS: The analysis of WM structural images found no morphological differences between dentulous and edentulous subjects. However, significant atrophy of GM was observed in the hippocampus, caudate nucleus and temporal pole of the right hemisphere in edentulous subjects. CONCLUSIONS: The results of this study suggest that tooth loss was a causal factor for volume reduction in brain areas related to memory, learning and cognition.
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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 | 0.000 | 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".