Effect of topical fluoride application on enamel after in‐office bleaching, as evaluated using a novel hardness tester and a transverse microradiography method
Bibliographic record
Abstract
This study evaluated the effect of topical fluoride application on enamel hardness after in-office bleaching. Twelve human incisors were cut along the long axis, resulting in 24 halves used in four treatment groups (n = 6 in each group): (i) untreated group (C); (ii) in-office bleaching material (B); (iii) treatment with surface reaction-type prereacted glass-ionomer varnish after in-office bleaching (B+PRG); and (iv) treatment with acidulated phosphate fluoride solution after bleaching (B+F). All specimens were subjected to pH-cycling for 4 wk. Knoop hardness was measured using a Cariotester. The decalcification of enamel was assessed quantitatively by measuring the integrated mineral loss (ΔIML). Games-Howell analysis was used to assess statistical significance of between-group differences. The Knoop hardness decreased significantly after bleaching for all groups. In treatment groups B+PRG and B+F, the Knoop hardness returned to the original unbleached values after the first pH cycle and did not change afterwards. In treatment groups C and B there was a gradual decrease in the Knoop hardness until the fourth pH cycle. The integrated mineral loss, ΔIML, was significantly higher in treatment group B+F after 2 wk than in the other treatment groups. After 4 wk, the ΔIML in treatment group B was significantly higher than in treatment group B+PRG. The application of fluoride-containing materials after bleaching results in recuperation of hardness to levels similar to those of unbleached enamel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".