Interleukin-6 levels in the serum and saliva of patients with oral lichen planus compared with healthy controls: a meta-analysis study
Bibliographic record
Abstract
Interleukin-6 (IL-6) is a cytokine that contributes to the pathogenesis of oral lichen planus (OLP). The aim of this meta-analysis study is the evaluation of IL-6 levels in the serum and saliva of patients with OLP compared with healthy controls. We searched the studies in 5 databases: PubMed/Medline, Scopus, ScienceDirect, Web of Science, and Cochrane Library, from 1983 to Oct 31, 2016. Eleven studies were analysed for the meta-analysis study. The reviewers independently evaluated the quality of each included study using the Newcastle-Ottawa Quality Assessment Scale (NOS). A random-effects meta-analysis, using Comprehensive Meta-Analysis software version 2.0, was used to reflect the variation in studies. Heterogeneity between estimates was evaluated by the Q and I2 statistics and for the Q statistic; heterogeneity was considered for p < 0.1. Eleven studies included 529 OLP patients and 333 healthy controls. The review identified two different biomaterials used for IL-6 assays: saliva and serum. The mean quality score of eleven studies was 7 (high quality). Estimates pooled from 6 studies showed significant high saliva IL-6 levels in OLP patients compared with healthy controls (the standardised difference in means (SDM) = 4.534, 95% CI = 1.915-7.153, p = 0.001). Also, estimates pooled from 7 studies showed significantly high serum IL-6 levels in OLP patients compared with healthy controls (SDM = 1.482, 95% CI = 0.524-2.439, p = 0.002). The higher levels of IL-6 in saliva compared with serum suggest that measurement of this marker in saliva may be more useful than serum for diagnostic and therapeutic aims.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.058 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".