Determinants of post-stroke cognitive impairment in patients with periodontal disease
Bibliographic record
Abstract
INTRODUCTION: Periodontal disease (PD) is a risk factor for stroke and cardiovascular disease. The effect of PD on post-stroke cognitive impairment (PSCI) remains underexplored. METHODS: A cross-sectional analysis of the Periodontal tReatment to Eliminate Minority InEquality and Rural disparities in Stroke (PREMIERS) study participants was conducted. Baseline cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) within 90 days of the index event. MoCA score ≤21 indicated severe PSCI. White matter hyperintensity, indicating cerebral small vessel disease (cSVD), was evaluated using the Fazekas scale on MRI. Due to non-normal MoCA distribution, two analytical approaches were employed: 1) logistic regression using dichotomized MoCA scores based on clinically relevant cutoffs and 2) generalized linear mixed modeling after bootstrap normalization that examined MoCA scores continuously. RESULTS: Among 280 participants with PD, 48% exhibited severe PSCI. Both analytical approaches demonstrated that severe PD, African American (AA) race, and greater stroke severity significantly and independently predicted severe PSCI, while advanced education was protective. Fazekas' scale showed no significant associations with PSCI. CONCLUSIONS: This study identifies PD severity as a novel and independent contributor to early PSCI. Traditional predictors like AA race, educational attainment, and stroke severity remained significant. CLINICAL TRIAL REGISTRATION INFORMATION: https://www. CLINICALTRIALS: gov; Unique identifier: NCT02541032.
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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 | 0.000 |
| 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".