A Cross-Sectional Study of the Relationship Between Low-Level Live Microbe Dietary Intake and Periodontitis among US Adults: Results from the NHANES 2009–2014
Bibliographic record
Abstract
Background: The dietary intake of live microbes is inextricably linked to human health. The aim of this study was to investigate a potential relationship between low-level dietary live microbe (LDLM) intake and periodontitis. Methods: According to the Sanders dietary live microbial classification system and the Centers for Disease Control and Prevention and American Academy of Periodontology periodontitis case definitions, this observational study included a total of 3,116 adults from the National Health and Nutrition Examination Survey (NHANES) database with complete periodontal data and LDLM intake. Univariate analysis, multivariable logistic regression analysis, and restricted cubic spline regression were performed to analyze the association between periodontitis and LDLM. Results: After adjusting for all covariates, LDLM was found to be associated with periodontitis prevalence ( P = 0.05 for trend) but not with periodontitis severity. The results of the restricted cubic spline analysis showed a non-linear relationship between LDLM and periodontitis prevalence ( P = 0.05). Periodontitis prevalence was lowest with LDLM consumption of 2,923.89 g per day. Conclusion: LDLM intake showed a statistically significant non-linear relationship with periodontitis prevalence, with the lowest prevalence of periodontitis at an intake of 2,923.89 g per day.
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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 | 0.000 |
| 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 | 0.001 |
| 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".