Is Fluoride Concentration in Dentin and Enamel a Good Indicator of Dental Fluorosis?
Bibliographic record
Abstract
Despite some studies correlating dental fluorosis (DF) and fluoride (F) concentration in dental enamel, no information is available about DF and dentin F concentration. Our objective was to determine the correlation between teeth F concentration and DF severity in unerupted human 3rd molars, and the correlation between dentin and enamel F concentrations in the same tooth. Ninety-nine 3rd molars were studied-53 from Fortaleza, Brazil (F water, 0.7 ppm), 22 from Toronto (1.0 ppm), and 24 from Montreal (0.2 ppm). DF severity was evaluated according to the Thylstrup-Fejerskov Index, while F concentration was analyzed by Instrumental Neutron Activation Analysis. DF severity varied between TF0 and TF4, while F concentration ranged between 39 and 550 ppm in enamel and 101 and 860 ppm in dentin. Our results showed correlation between dentin F concentration and DF (r(S) = 0.316, p = 0.001), but no correlation between enamel F concentration and DF (r(S) = 0.154, p = 0.133). No correlation was observed between dentin and enamel F concentrations in the same tooth (r(S) = 0.064, p = 0.536).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".