Efficacy of Light-Activated Sealant on Enamel Demineralization in Orthodontic Patients: An Atomic Force Microscope Evaluation
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to investigate the efficacy of (Pro Seal) sealant in preventing enamel decalcification in-vivo and compare its effect with fluoride varnish and unfilled sealant using atomic force microscopy. MATERIALS AND METHODS: Eight orthodontic patients who were candidates for extraction of all first premolars for orthodontic treatment were recruited to this study. Thirty two premolars (upper and lower) were randomly divided into four groups (n=8) for each group, 4 maxillary and 4 mandibular); Control (no -treatment); Fluoride varnish, Unfilled sealant (Light Bond) and filled sealant (Pro-Seal). After two months the brackets were debonded and the teeth were extracted and prepared for Atomic force microscopic scanning. Each sample was scanned twice at two different scan areas 50 and 10µm at the buccal cervical third of the crown. Images were recorded with slow scan rate and resolution and the mean roughness height and total surface area were calculated for each scan area. Comparison between groups was performed using one way analysis of variance test with level of significance was set to be 0.05. RESULTS: Pro Seal treated samples show the lowest roughness height and total surface area. CONCLUSION: Pro Seal was the most effective prophylaxis technique in preventing enamel demineralization during orthodontic treatment.
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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 | 0.000 | 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".