Association between metabolic syndrome and gingival inflammation in obese children
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Our previous work showed a positive association between metabolic syndrome (MetS) and gingival crevicular fluid (GCF) tumour necrosis factor-alpha (TNF-α) in a sample of obese and non-obese children. However, whether this association persists among obese children is unknown. We aim to investigate the extent to which MetS is associated with GCF TNF-α level among obese children. METHODOLOGY: We performed a cross-sectional analysis using data from visit 1 of the QUebec Adipose and Lifestyle InvesTigation in Youth cohort. A total of 219 obese children aged 8-10 years, for whom data were available for both MetS and TNF-α, were included in our analysis. The independent variable, MetS, was defined according to the International Diabetes Federation recommendations. GCF samples were collected from the gingival sulcus using a paper strip, and the concentration of TNF-α was determined by enzyme-linked immunosorbent assay. Analyses included descriptive statistics and sex-specific linear regression analyses adjusting for potential confounders. RESULTS: In this sample comprising only obese children, 24 (10.9%) had MetS. Among obese boys, those with MetS had 44.9% higher GCF TNF-α (95% confidence interval: 16.5%-73.3%) compared to those without MetS. No such association was detected in obese girls. CONCLUSION: MetS was positively associated with GCF TNF-α concentration in obese boys. These results suggest that obese boys with MetS may have a worse gingival health profile compared to their obese counterpart without MetS.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".