Alcohol consumption is associated with periodontitis. A systematic review and meta-analysis of observational studies.
Bibliographic record
Abstract
OBJECTIVE: To determine whether alcohol consumption is associated with the risk of periodontitis. BASIC RESEARCH DESIGN: Systematic review and meta-analysis of observational studies performed using the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. METHOD: PubMed and Scopus were searched for eligible articles published in English from inception till November 2018. The quality of studies was assessed by the Newcastle Ottawa Scale. Pooled odds ratios (OR) and 95% confidence intervals (CI) were calculated for the risk of periodontitis associated with highest versus lowest/non-alcohol in a random effects meta-analysis model. Heterogeneity and sensitivity were investigated in meta regression analysis. A funnel plot was used to assess publication bias. RESULTS: Twenty-nine observational studies were included. One study with two separate datasets was considered as two separate studies for analysis. Alcohol consumption was significantly associated with the presence of periodontitis (OR = 1.26, 95% CI= 1.11-1.41). Significant heterogeneity (I2=71%) was present in the overall analysis, primarily attributable to sampling cross-sectional studies (I2=76.6%). A funnel plot and Egger tests (p=0.0001) suggested the presence of publication bias. CONCLUSION: Alcohol consumption was associated with increased occurrence of periodontitis and should be considered as a parameter in periodontal risk assessment. Publication bias should be explored in future studies.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".