Evaluating Interleukin-Driven Soft Tissue Response in Clear Aligners and Fixed Appliances: A Schematic Study and Meta-Analysis
Bibliographic record
Abstract
Background: Orthodontic appliances have been known to affect periodontal health; this is because they have been shown to change the amount of plaque and local inflammatory response. This review tried to compare the periodontal condition and gingival crevicular fluid (GCF) cytokine concentration in individuals using clear aligners and fixed orthodontic anchors. Methods: A meta-analysis and systematic review of studies was done by searching the databases (PubMed, Scopus, Web of Science, Cochrane Library) to determine the number of research articles published until 2025. Clinical studies with observational studies comparing periodontal clinical outcome indexes and cytokine levels (e.g., IL-1, TNF-alpha) in patients with clear aligners and fixed appliances were obtained. Extracted data were analyzed, the risk of bias was calculated using Newcastle-Ottawa scale and Cochrane risk of bias tool, and a random-effects meta-analysis was done using RevMan 5.4.1. A 95% confidence interval (CI) of the standardized mean difference (SMD) was also computed. Results: Six studies with 237 participants were found that fit the inclusion criteria. The meta-analysis showed that patients who used clear aligners had a much-reduced level of pro-inflammatory cytokines when compared to the level of the same in the fixed appliances group. Furthermore, clinical values such as plaque index, gingival index, and bleeding on probing were better in the clear aligner group. Discussion: Clear aligners can be periodontally beneficial as they generate reduced tissue inflammation rates or clinical periodontal outcomes in comparison with fixed appliances in the course of orthodontic treatment. The small size of well-conducted studies and the implications of measurement methods of cytokines might have an influence on the generalizability of research results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".