Fluoride release from light-cured orthodontic bonding materials
Bibliographic record
Abstract
The purpose of this study was to compare the rate of fluoride release with time of one non-fluoridated and three fluoride-containing orthodontic bonding materials in distilled water and artificial saliva. Materials tested were: Assure (Reliance, Itasca, IL), Fuji Ortho LC (GC America Inc., Alsip, IL), Python (TP Orthodontics Inc., LaPorte, IN), and Transbond XT (3M, St Paul, MN). Twenty specimens of each material were polymerized and placed in polyethylene tubes. Half the specimens were stored in 1 mL of distilled water and half in 1 mL of unstimulated artificial saliva, at 37C and 100% relative humidity. Fluoride release was measured with an ion-specific electrode. Readings were taken-at 1, 2, 3, 5, 7 and 9 days from time of immersion, then weekly for three weeks and monthly for 5 months. To prevent cumulative measurements, storage solutions were changed 24 h prior to the weekly and monthly readings. Results showed Assure to release the most fluoride, followed by Fuji Ortho LC, Python, and Transbond. The fluoride release rates were greatest during the first days of testing, declining to low but stable levels. The type of storage medium did not dramatically affect fluoride release. Throughout the study, daily fluoride release rates of all three fluoride-containing materials were within the therapeutic range for the reduction of enamel demineralization. The second part of the study tested the twenty samples of Assure for a further two-week period, after exposure to running and still distilled water. Although fluoride release rates declined with time, they were again within the therapeutic range. Release rates were similar in running and still water at all time points.
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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 | 0.001 |
| 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 | 0.001 |
| 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".