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 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) | 0.001 | 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".