Effect of tooth bleaching on shear bond strength of a fluoride-releasing sealant
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of an in-office plus at-home bleaching protocol on shear bond strength of orthodontic buttons when using a fluoride-releasing sealant. MATERIALS AND METHODS: Extracted human molars (160) were randomly divided into bleached (n = 80) and unbleached groups (n = 80). The bleached group was treated with 45% carbamide peroxide for 30 minutes, followed by five applications of 20% carbamide peroxide at 24-hour intervals. After 2 weeks, lingual buttons were bonded on the teeth in both groups using either Transbond XT primer or Pro Seal sealant. The teeth were then stored in artificial saliva and subjected to shear testing at 24 hours and 3 months using a Zwick Universal Test Machine. Comparisons of mean shear bond strength values were made with the analysis of variance test. The Fisher's exact test was used to evaluate the adhesive remnant index scores. RESULTS: The analysis of variance of the 24-hour results indicated a significant difference between the four subgroups (P < .0011). Further simple t-tests indicated that the differences were significant only between bleached and unbleached subgroups (P < .0011). The 3-month results showed the mean shear bond strengths of the unbleached group using Pro Seal sealant was significantly lower than that of the other, though still greater than clinically minimal suggested bond strengths. Interestingly, 15% of the bleached teeth exhibited enamel fracture at the 3-month testing. CONCLUSION: Both Pro Seal sealant and Transbond XT primer demonstrated reliable shear bond strength values on both bleached and unbleached teeth over time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".