Serum vitamin D levels and chronic periodontitis in adult, Caucasian population—a systematic review
Bibliographic record
Abstract
The aim of this systematic review was to explore the relationship between serum vitamin D levels and periodontal disease. The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The focused PICO question was: Are serum vitamin D levels (I) associated with gingivitis or periodontitis (C) in healthy humans (P)? PubMed (Medline), Embase, Science Direct, the Cochrane library, and grey literature were searched up to 31 July 2017. Two reviewers independently selected studies and extracted data. Quality, risk of bias, and heterogeneity of the studies were assessed using the Newcastle-Ottawa Scale. In total, 365 studies were identified and 24 were analyzed. Seven studies fulfilled the inclusion criteria. Four case-control studies showed an influence of vitamin D and its metabolites on periodontal health status/disease. One interventional study suggested the proposed anti-inflammatory role of vitamin D. Two cross-sectional studies failed to show a relationship between vitamin D and periodontal condition. The literature on the effect of serum vitamin D levels on periodontal status remains scarce and controversial. Some data, however, support a "perio-protective" role for vitamin D. There is a need for well-designed randomized clinical trials to explore the possible anti-inflammatory effect of vitamin D on periodontal health.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".