Multiple tooth loss is associated with vascular cognitive impairment in subjects with acute ischemic stroke
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Emerging evidence shows that tooth loss is associated with cognitive impairment and dementia. Vascular cognitive impairment (VCI) is a common consequence of ischemic stroke. This study investigated the association of tooth loss with VCI in patients with acute stroke. MATERIAL AND METHODS: A total of 161 subjects with acute ischemic stroke were recruited. Within 1 wk after admission, fasting blood tests were undertaken and the number of teeth present was recorded. VCI was evaluated with the Montreal Cognitive Assessment (MoCA). RESULTS: The patients with loss of ≥ 8 teeth exhibited significantly lower MoCA values as compared to those with loss of ≤ 7 teeth (13.2 ± 6.6 vs. 17.3 ± 6.0, p < 0.001). Multivariate logistic regression analysis showed that loss of ≥ 8 teeth (OR = 3.1, 95% CI: 1.2-7.9, p = 0.02) and stroke history (OR = 3.8, 95% CI: 1.1-14.1, p = 0.04) were significantly associated with VCI (MoCA score ≤ 20.0). CONCLUSION: Within the limitations of this study, the current findings provide the first evidence that multiple tooth loss is independently associated with VCI in patients with acute ischemic stroke.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".