A meta-analysis of emotional disorders as possible risk factors for chronic periodontitis
Bibliographic record
Abstract
The aim of the present meta-analysis was to evaluate scientific evidence on the association between emotional disorder (depression and anxiety) and chronic periodontitis. An overall electronic literature search in PubMed, ISI Web of Science, Cochrane Library, and China National Knowledge Infrastructure was undertaken up to November 2017. Newcastle-Ottawa scale was applied to ascertain the validity of each eligible study. Stata statistical software was used to perform meta-analysis. The strength of the association between periodontitis and emotional disorder was measured by odds ratios (ORs) with their 95% confidence intervals (95% CIs). Subgroup analysis and sensitivity analysis were performed. Publication bias was assessed through funnel plots and Begger's test. A total of 14 eligible articles were included in the meta-analysis, 6 of them were focused exclusively on depression, whereas 8 studies investigated both depression and anxiety. There was significant association between emotional disorder and chronic periodontitis (OR = 1.54, 95% CI = 1.27-1.86). Sensitivity analyses confirmed the stability of the present results. No evidence of asymmetry was observed in Begger's test. This meta-analysis demonstrates significant association between emotional disorder (including anxiety and depression) and chronic periodontitis. Nevertheless, the result should be interpreted with caution because of the potential bias and confounding in the included 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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.042 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".