Improving esthetically objectionable human enamel fluorosis with a simple microabrasion technique
Bibliographic record
Abstract
Mild-to-moderately severe enamel fluorosis (EF) is an unsightly maturation-phase dental disorder. Despite extensive epidemiological studies on EF, little is known about individual treatment options. This study was carried out to determine whether a simple microabrasion technique is effective in improving the esthetics of EF. Patients with a variety of severities were treated using a water-cooled fine diamond polishing bur at high speed to remove the surface enamel layers. Photographs of the affected teeth before and after treatment were shown by computer to a panel of three judges (two lay and one experienced), who rated the appearance of the teeth using a newly developed visual analog scale. The severity of EF was rated randomly and blind for 52 individual teeth (26 before and 26 after treatment). Reteated-measures analysis of variance was used to analyze the results. The lay judges rated the appearance of the teeth with EF as significantly more objectionable before treatment. All judges found a significant improvement in the severity of EF after treatment. Using a newly developed visual analog scale, our study indicates that EF of an objectionable nature can be significantly improved with a simple microabrasion technique, thus conserving tooth structure and minimizing the cost of treating EF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".