Changes in salivary biomarkers of pain, anxiety, stress, and inflammation related to tooth movement during orthodontic treatment: a systematic review
Bibliographic record
Abstract
OBJECTIVE: This systematic review aimed to analyze the literature on changes in endogenous salivary biomarkers of pain, anxiety, stress, and inflammation related to tooth movement during orthodontic treatment of children and adolescents. MATERIAL AND METHODS: An electronic search was performed in nine databases to identify quasi-experimental studies, without restricting publication language and year. Two reviewers extracted the data and assessed the individual risk of bias using the JBI tools, and the certainty of evidence using the GRADE tool. RESULTS: The electronic search found 7,038 records, of which 12 met the eligibility criteria and were included in the qualitative synthesis. Most studies had a low risk of bias. Biomarkers were grouped into five categories: electrolytes, enzymes, hormones, immunoglobulins, and mediators. Electrolytes showed decreased Ca2+, Pi3+ and K+ levels, and increased Na+ and Cl- levels. All enzymes (ALP, LDH, MMP8, and MMP9) increased over time. Hormones presented a decrease in leptin and some fluctuations in daily cortisol levels. Immunoglobulins (IgA, IgG, IgM, IgD, and IgE) had no significant changes, and salivary IgA showed divergent results among studies. Mediators (sRANKL, OPG, IL-1β, and PGE2) showed fluctuations at different treatment stages, mainly after orthodontic activation. CONCLUSIONS: Based on a very low certainty level, orthodontic tooth movement had little to no effect on endogenous salivary biomarkers.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".