Tooth loss and dementia amongst older adults residing in long‐term care facilities in Vancouver: A case‐control study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this case-control study was to determine the association between dementia and the number of missing teeth, functional occlusal units and denture use in older adults residing in Long-Term Care (LTC) facilities. BACKGROUND: Many studies have shown an association between dementia and tooth loss. However, few studies with a large sample size have been reported describing the relationship between dementia and the number of missing teeth, remaining teeth and functional occlusal units. METHODS: An oral health assessment database of 2160 older adults admitted to LTC facilities in Vancouver, Canada, between 2015-2019 was utilised. Participants with a diagnosis of dementia in their medical records (N = 1174) were compared to those without dementia (N = 986). Multiple logistic regression analysis was used to explore a potential association between the number of missing teeth, functional occlusal units and the use of dentures and dementia. RESULTS: The number of remaining teeth (OR = 1.0, 95% Confidence Interval = 1.0-1.0; P = .054) and number of functional occlusal units (OR = 1.0, 95% CI = 1.0-1.0; P = .059) were not associated with dementia after adjusting for age, sex, oral self-care and systemic conditions. Denture use (OR = 1.1, 95% CI = 0.5-2.4; P = .790) was not associated with dementia in edentulous patients. CONCLUSION: There was no association between dementia and the number of remaining teeth, functional occlusal units or wearing dentures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".