Where periodontitis meets metabolic syndrome—The role of common health‐related risk factors
Bibliographic record
Abstract
OBJECTIVES: To analyse and compare associations between metabolic syndrome (MetS) and its components in periodontitis compared to control patients. METHODS: This 7-year cross-sectional study retrospectively analysed medical records of 504 individuals aged 18-90 who attended the student dental clinic between 2008 and 2014. Demographics, smoking habits, blood pressure, waist circumference, as well as presence of: periodontitis, MetS, diabetes, hypertension, hyperlipidaemia, stroke, heart disease, cancer and psychiatric disorders were recorded. RESULTS: The study population composed of 231 (45.8%) males and 273 (54.2%) females, with an average age of 55.79 ± 16.91 years. A patient profile associated with periodontitis was identified and included male sex, older age, smoking, higher smoking pack-years, abdominal obesity, higher systolic and diastolic blood pressures, the presence of MetS or its components, hypertension, hyperlipidaemia, diabetes or diseases associated with its consequences such as ischaemic heart disease and stroke. Following multivariate logistic regression analysis, age and smoking retained a significant association with periodontitis, whereas the systemic disorders did not. CONCLUSIONS: The association between periodontitis and MetS may be explained by shared common profile and risk factors. An appropriate risk factors management approach should be adopted by both dental and general health clinicians and health authorities, to control common high-risk behaviours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".