Serum and Salivary IgA, IgG, and IgM Levels in Oral Lichen Planus: A Systematic Review and Meta-Analysis of Case-Control Studies
Bibliographic record
Abstract
Immunoglobulins (IgA, IgG, and IgM) are significant anti-inflammatory factors. The meta-analysis aimed to assess the serum and salivary levels of Igs as more important immunoglobulins in patients affected by oral lichen planus (OLP) compared to the healthy controls. Four databases, including PubMed/Medline, Scopus, Web of Science, and Cochrane Library as well as Iranian databases were checked up to January 2018 without language restriction. The quality of each involved study was done using the Newcastle–Ottawa Quality Assessment Scale (NOS) questionnaire. A random-effects model analysis was done by RevMan 5.3 software applying the mean difference (MD) plus 95% confidence intervals (CIs). The CMA 2.0 software was applied to calculate the publication bias among the studies. Out of 70 studies found in the databases, 8 studies were involved and analyzed in the meta-analysis. The meta-analysis included 282 OLP patients and 221 healthy controls. The pooled MDs of serum levels of IgA, IgG, and IgM were −0.13 g/L [95% CI: −0.24, −0.02; P = 0.02], 1.01 g/L [95% CI: −0.91, 2.93; P = 0.30], and −0.06 g/L [95% CI: −0.25, 0.14; P = 0.56], respectively; whereas, the salivary IgA and IgG levels were 71.54 mg/L [95% CI: 12.01, 131.07; P = 0.02] and 0.59 mg/L [95% CI: −0.20, 1.38; P = 0.14], respectively. Considering the few studies performed on saliva, the results suggested that the salivary levels, especially IgA level had higher values than the serum levels. Therefore, the salivary immunoglobulins can play a significant function in the OLP pathogenesis.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.002 | 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".