Precursors of insulin resistance underlying periodontitis in adolescents aged 17–18 years
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between insulin resistance markers and periodontitis in adolescents, analyzing confounder variables and the adiposity as a mediator. METHODS: This is population-based study is representative of adolescents aged 17-18 years from public schools in São Luís, Brazil (n = 405). Insulin resistance was assessed using the Model of Assessment of the Homeostasis of the Insulin Resistance Index (HOMA-IR) and its percussor triglycerides/HDL-cholesterol ratio (TG/HDL-c). The outcome was Initial Periodontitis, a latent variable estimated by the common variance shared among bleeding on probing, probing depth ≥ 4 mm, and clinical attachment loss ≥ 4 mm. The association between insulin resistance and Initial Periodontitis was modeled via pathways triggered by socioeconomic status, smoking, alcohol, and Adiposity, using structural equation modeling. RESULTS: Higher TG/HDL-c was directly associated with higher Initial Periodontitis (standardized coefficient [SC] = 0.130, p < 0.001). HOMA-IR was not associated with periodontal outcome (SC = 0.023, p = 0.075), but it was with Adiposity (SC = 0.495, p < 0.001). Higher TG/HDL-c was associated with Adiposity (SC = 0.202, p < 0.001). CONCLUSION: The insulin resistance markers were associated with early signs of periodontal breakdown among adolescents, suggesting a possible relationship between diabetes and periodontitis commences early in life.
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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 | 0.001 | 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".