Association of vitamin D levels and oral lichen planus. Systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Oral lichen planus (OLP) is an inmuno-mediated mucocutaneous chronical inflammatory disease. Multiple predisposing factors are considered, such as autoimmune response, microorganisms, medications, dental materials, psychological stress, genetic predisposition or nutritional deficiencies. The deficiency of vitamin D has been related to various autoimmune diseases like OLP. MATERIAL AND METHODS: The electronic search was conducted in the MEDLINE (Pubmed), Scopus, Cochrane Library and Web of Science databases. To assess any potential risk of bias, the authors critically appraised each study by the Newcastle-Ottawa Scale for cohort and case-control studies. Pooled analyses were performed using a random-effects model. Heterogeneity of the studies was assessed by the I2 statistics. Forest Plots were performed to graphically represent the difference between vitamin D concentrations in the OLP compared to healthy group, with a 95% confidence interval. RESULTS: After applying our inclusion and exclusion criteria, 7 articles were included in our review. The median concentration vitamin D in ng/ml found in serum for patients with OLP was of 26,6311,75ng/ml and for healthy patients was of 31,438,7ng/ml. Regarding the quantitative analysis, 7 studies were included. The difference in the concentration of vitamin D in healthy patients and patients with OLP statistically significant (Weighted Mean Difference (WMD): -6.20, 95% CI: -11.24 to -1.15, p=0.02 and I2 heterogeneity: 94%, p<0.00001). CONCLUSIONS: The patients with OLP have statistically lower vitamin D levels than healthy patients.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".