Abstract TP32: Tooth Loss is Associated with Post-Stroke Cognitive Impairment
Bibliographic record
Abstract
Background: Periodontal disease and dental caries are a leading cause of tooth loss which has been correlated with stroke in the REGARDS study. We investigated the correlation between tooth loss and post-stroke cognitive impairment (PSCI) assessed by Montreal Cognitive Assessment (MoCA). Methods: The MoCA was conducted in consecutive ischemic stroke and TIA patients (N=280) enrolled in PREMIERS trial (ClinicalTrials.gov NCT#02541032) based on presence of moderately severe periodontal disease. These patients were categorized as having normal/mild cognitive impairment (MoCA>19) or severe cognitive impairment (MoCA ≤19). Regarding tooth loss, patients were categorized into two separate groups based on the number of teeth lost as noted during initial assessment. The groups were categorized into those reporting significant tooth loss (≥8) and no significant tooth loss of <7. We tested the association between tooth loss to the MoCA assessment in post-stroke/TIA patients. In addition to the clinical cofounders' patients were assessed for periventricular and deep white matter hyperintensity using axial FLAIR images. They were graded on a 3-point Fazekas scale; none/mild disease = Fazekas grades 0-1 and moderate/severe disease = Fazekas grades 2-3. Univariate and multivariable logistic regression analysis were conducted to calculate crude and adjusted Odds Ratio (OR) respectively. Results: We compared those with significant tooth loss (N=201) with non-significant tooth loss group (N=79). Those that scored severe on the MoCA assessment were only slightly older than those who scored normal/mild (age 61± 12 vs. 59 ± 12, respectively), slightly less likely to be male (63% vs. 64%, respectively), and more likely to be African American (83% vs. 68%, respectively). Significant tooth loss was associated with severe PSCI (Crude OR=3.25, 95% CI.; 1.65-6.41). The association remained significant after adjustment for age, sex, race, level of education, hypertension, diabetes, white matter hyperintensity assessed by Fazeka scale (Adjusted OR=2.60, 95% CI; 1.20-5.61). Conclusion: In summary, the results showed a significant independent association between significant tooth loss and PSCI in a patient population with moderate to severe periodontal disease. Further study is needed to see if modifying tooth loss by aggressive management of periodontal disease and dental caries could alleviate PSCI.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.006 | 0.001 |
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".