The association between undiagnosed glycaemic abnormalities and cardiometabolic risk factors with periodontitis: results from 2007–2009 Canadian Health Measures Survey
Bibliographic record
Abstract
AIM: The aim was to investigate the association between undiagnosed glycaemic abnormalities and cardiometabolic risk factors with periodontitis. METHODS: Using Cycle 1 (2007-2009) of Canadian Health Measures Survey, survey-sampling weights were applied to a restricted sample of fasting, non-pregnant adults between 19 and 79 years of age without diagnosed or treated type 2 diabetes. We estimated the prevalence of periodontitis and various cardiometabolic risk factors according to the clinical diagnostic definition for metabolic syndrome (MetS), recognized by the American Heart Association and National Heart, Lung, and Blood Institute. Adjusted logistic regression models were used to estimate prevalence odds ratios (PORs) examining the association between cardiometabolic risk factors and periodontitis among dentate adults with available attachment loss measures. RESULTS: The prevalence of combined moderate-to-severe periodontitis was 17.93% (95% CI 15.85, 20.02). Hyperglycaemia (fasting plasma glucose (FPG) ≥ 5.6 mmol/l) was significantly associated with periodontitis, POR = 1.60 (95% (CI) 1.04, 2.45), but was no longer significant after controlling for socioeconomic status variables. Central adiposity, dyslipidaemia and hypertension were not associated with periodontitis. CONCLUSION: Glucose disruption measured by FPG was associated with periodontitis; however, no association was observed with other cardiometabolic risk factors or MetS in a cross-sectional, nationally representative sample of Canadian adults.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".