Association between depression and periodontitis: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: The aim of this systematic review and meta-analysis was to assess the scientific evidence on the association between depression and periodontitis. METHODS: An electronic search was conducted in three databases until October 2015 (PROSPERO-CRD42014006451). Hand searches and grey literature were also included. Search retrieved 423 potentially studies. Two independent reviewers selected the studies, extracted data and assessed risk bias through a modified version of Newcastle-Ottawa scale. Meta-analysis was performed for the presence/absence of periodontitis (dichotomic). Summary effect measures and odds ratio (OR) 95% CI were calculated. RESULTS: After selecting the studies, 15 were included in the systematic review (eight cross-sectional, six case-control and one cohort study). Six studies reported that depression was associated with periodontitis, whereas nine studies did not. The majority of studies had low risk of bias by methodological quality assessment. Meta-analysis of seven cross-sectional studies showed no significant association between depression and periodontitis (OR = 1.03, 95% CI = 0.75-1.41). CONCLUSION: Findings from the present systematic review showed a great heterogeneity among the studies and the summary effect measure of the meta-analysis cannot affirm an association between depression and periodontitis. Future studies with different designs in distinct populations should be conducted to investigate this association.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.026 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".