Assessment of shear bond strength of brackets bonded by direct and indirect techniques: an in vitro study
Bibliographic record
Abstract
OBJECTIVE: This in vitro study was designed to evaluate the shear bond strength (SBS) of orthodontic metal brackets bonded by direct and indirect techniques. METHODS: Thirty healthy human maxillary premolar teeth were used. The teeth were divided into three groups of 10 teeth each: Group I - indirect bonding with SondhiTM Rapid-Set system (3M/Unitek), Group II - indirect bonding with TransbondTM XT adhesive system (3M/Unitek) and Group III - direct bonding with TransbondTM XT adhesive system (3M/Unitek). After bonding and obtaining the specimens for the study, the specimens were subjected to SBS testing in a universal testing machine (Emic, model DL-500). The Kolmogorov-Smirnov test was applied to ascertain that the data had a normal distribution and the Bartlett test to check whether there was homogeneity of variance. One-factor analysis of variance was performed and, subsequently, Tukey's test for paired means. A 5% significance level was adopted. RESULTS: The results of Group I were 67.6 (N) and 5.9 (MPa); Group II, 68.9 (N) and 6.1 (MPa) and Group III (control), 92.5 (N) and 8.1 (MPa). CONCLUSION: It can therefore be concluded that the means for Group III were significantly higher compared with Groups I and II in both Newton (N) and Megapascal (MPa) values. The means attained by the indirect bonding technique used in Groups I and II, however, exhibited no statistically significant differences.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 0.001 |
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".