How Does Fluoride Affect Dentin Microhardness and Mineralization?
Bibliographic record
Abstract
Fluoride (F) has been a useful instrument in caries prevention. However, only limited data exist on the effect of its long-term use on dentin mineralization patterns and microhardness. The objective of this study was to evaluate the influence of tooth F concentration ([F]) and dental fluorosis (DF) severity on dentin microhardness and mineralization. We collected 137 teeth in Montreal and Toronto, Canada, and Fortaleza, Brazil, where optimum or suboptimum levels of water F were 0.2 ppm, 1 ppm, and 0.7 ppm, respectively. Teeth were analyzed for DF severity, dentin [F], enamel [F], dentin microhardness, and dentin mineralization. Dentin [F] correlated with DF severity; enamel [F] correlated with dentin microhardness and dentin mineralization; DF severity correlated with dentin microhardness. Genetic factors (e.g., DF severity) and environmental factors (e.g., tooth [F]) influenced the mechanical properties (microhardness) of the teeth, while only the environmental factors influenced their material properties (e.g., mineralization). Fortaleza teeth were harder and less mineralized and presented higher dentin [F] values. Montreal teeth presented lower levels of DF when compared with both Toronto and Fortaleza teeth.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".