Photobiomodulation of gingival lesions resulting from autoimmune diseases: systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effects of photobiomodulation (PBM) in gingival lesions resulting from autoimmune diseases; to compare PBM and topical corticosteroid (CS) treatment; and to assess PBM outcome over time of follow-up. MATERIALS AND METHODS: A comprehensive electronic search was performed in four electronic databases. Treatment effects were measured through visual analog scale of pain (VAS) and clinical evolution of lesion (Thongprasom scale for oral lichen planus (OLP)). Meta-analysis was performed to compare PBM with topical corticosteroid treatment and to evaluate PBM effect over time of follow-up. RESULTS: Seventeen studies were included in this review, of which six were used for the meta-analysis. Meta-analysis results showed no significant differences between PBM and topical CS in pain reduction at baseline (MD = 0.20, 95% CI = - 0.92, 1.32, p = 0.72) and 60-day follow-up (MD = 0.63, 95% CI = - 3.93, 5.19, p = 0.79); however, VAS showed significant pain reduction when compared before and after PBM at 30-day (MD = - 3.52, 95% CI = - 5.40, - 1.64, p = 0.0002) and 60-day (MD = - 5.04, 95% CI = - 5.86, - 4.22, p < 0.00001) follow-up. Thongprasom clinical scale for OLP also showed significant improvement at 30-day follow-up (MD = - 2.50, 95% CI = - 2.92, - 2.08, p < 0.00001) after PBM. CONCLUSION: PBM led to significant reduction of pain and clinical scores of the lesions, not having shown significant differences when compared to topical CS. CLINICAL RELEVANCE: PBM has been used in the treatment of autoimmune gingival lesions, but so far there is little strong evidence to support its use.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.021 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".