Evaluation of Serum and Salivary Interleukin-6 and Interleukin-8 Levels in Oral Squamous Cell Carcinoma Patients: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This meta-analysis aimed to assess the salivary and serum concentrations of IL-6 and IL-8 in oral squamous cell carcinoma (OSCC) patients compared to the controls. Four electronic databases (Scopus, PubMed, Cochrane Library, and Web of Science) were searched up to January 2019. The study quality was checked according to the Newcastle-Ottawa Scale. The mean difference (MD) plus 95% confidence interval (95%CI) were calculated using RevMan 5.3 software. The publication bias and sensitivity analysis were done using CMA 2.0 software. Out of 309 studies retrieved from the 4 databases, 26 studies were analyzed in the present meta-analysis. In this meta-analysis, the pooled MD in the OSCC patients compared to the controls was 19.06 pg/mL (95%CI: 14.78-23.33) for the serum IL-6 level, 199.14 pg/mL (95%CI: 47.39-350.89) for the serum IL-8 level, 122 pg/mL (95%CI: 64-179) for the salivary IL-6 level, and 958 pg/dL (95%CI: 718-1197) for the salivary IL-8 level. All values in this meta-analysis were statistically significant. In conclusion, according to the meta-analysis results, the serum and salivary IL-6 and IL-8 levels in OSCC patients were significantly elevated compared to the controls, and both cytokines can be useful as potential biomarkers in early OSCC diagnosis.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.052 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".