The Effect of Various Lasers on the Bond Strength Between Orthodontic Brackets and Dental Ceramics: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/objective:This systematic review and meta-analysis aimed to assess how laser conditioning affected brackets bonded to dental ceramics' shear bond strength (SBS). Materials and methods:The study was conducted by searching Pubmed/Medline, Scopus, Embase, Web of Science, the Cochrane Library, and Google Scholar up to September 14, 2022. In addition, the reference lists of the relevant articles were checked manually. Articles that compared SBS of laser-treated feldspathic, lithium disilicate, or zirconia surfaces with other standard techniques for bonding metal or ceramic orthodontic brackets were considered. Using a random-effects model, data pooling was carried out as the weighted mean difference (WMD). Results:This study initially contained 1717 reports, and following review, 32 articles were deemed suitable for our meta-analysis. The pooling results showed that the treatments with lasers such as “Er:YAG” [WMD = −1.12 MPa; 95% confidence interval (CI): −1.93 to −0.31], “Er:YAG + Silane” (WMD = −3.08 MPa; 95% CI: −4.77 to −1.40), and “Nd: YAG + Silane” (WMD = −2.58 MPa; 95% CI: −3.76 to −1.40) had statistically significant lower adhesion values compared with controls. Contrarily, “Ti:Sapphire femtosecond” demonstrated significantly higher bonding values (WMD = 0.94 MPa; 95% CI: 0.29–1.60). In contrast, other interventions obtained no statistically significant difference in SBS. Conclusions:Most of the laser groups showed results comparable with those of conventional approaches. Although more research is necessary for definitive conclusions, laser treatment may be an effective option for treating the surfaces of ceramic materials.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.043 |
| 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".