Analysis of enamel/restoration interface submitted cariogenic challenge and fluoride release
Bibliographic record
Abstract
The treatment of high-risk patients still is a challenge. The understanding and development non-invasive, non-destructive, and non-ionizing techniques, can help to guide the treatment and the diagnosis of primary and recurrent caries. The present study evaluated the behavior of enamel/restoration interface after a cariogenic challenge by Fourier domain optical coherence tomography (FD-OCT), scanning electron microscopy (SEM) examination, and the fluoride release of the different restorative materials. Cavities (1.5 × 0.5 mm) were performed in enamel surface and divided into groups (n = 8): glass ionomer cement (GIC), resin-modified glass ionomer cement (RMGIC), and resin composite (RC). The samples were submitted to pH-cycling, and the solutions analyzed for cumulative fluoride by ion-analyzer. The morphology was analyzed by SEM through replicas. The optical attenuation coefficient (OAC) was calculated through exponential decay from the images generated by FD-OCT. Data were analyzed considering α = 0.05. OAC values increased for all groups after pH-cycling indicating demineralization (p < .05). Considering the remineralizing solution, RMGIC presented higher fluoride release rate, followed by GIC, while RC did not release any fluoride. Yet for the demineralizing solution, RMGIC and GIC released similar fluoride rates, overcoming RC (p < .05). Micrographs revealed no changes on the restorations margins, although enamel detachment was observed for RC and GIC after pH-cycling.
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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 | 0.000 |
| 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".