Markers of periodontal disease and risk of stroke: INTERSTROKE case-control study
Bibliographic record
Abstract
BACKGROUND: Periodontal disease may be an important modifiable risk factor for stroke. AIMS: To determine the contribution of markers of periodontal disease to stroke risk globally, within subpopulations, and by stroke subtypes. METHODS: INTERSTROKE is the largest international case-control study of risk factors for first acute stroke. All participants were asked a standardised set of questions about the presence or absence of painful teeth, painful gums or lost teeth, as markers of periodontal disease, within the previous year. The total number of reported variables was calculated per participant. Multivariable conditional logistic regression examined the association of these variables with acute stroke. RESULTS: In 26901 participants, across 32 countries, there was a significant multivariable association between lost teeth and stroke (OR 1.11, 95 % CI 1.01 - 1.22), but not painful teeth (OR 1.00, 95 % CI 0.91-1.10) or painful gums (OR 1.01, 95 % CI 0.89 - 1.14). When these symptoms were considered together there was a graded increased odds of stroke, with the largest magnitude of association seen if a patient reported all three of painful teeth, painful gums and lost teeth (OR 1.34, 95 % CI 1.00 - 1.79). CONCLUSIONS: Our findings suggest that features of severe periodontal disease are a risk factor for acute stroke. Periodontal disease should be considered as a potentially modifiable risk factor for 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".