Effectiveness of four home oral care productsfor primary enamel re-mineralization with earlycarious lesion: an in-vitro study
Bibliographic record
Abstract
Introduction: Management of dental caries during early stages is considered a clinical problem in young children.Objectives: The current in-vitro study assessed the influence of four home oral care products on re-mineralization of early caries lesions using enamel micro-hardness (EMH) test and morphological changes with scanning electron microscopy (SEM).Material and methods: Sixty primary canine teeth were selected and randomly divided into 5 groups (n = 12 in each group).Samples were de-mineralized, and toothpaste slurries were prepared to treat de-mineralized enamel with a pH-cycling model.The groups were divided as follows: Group 1.Control (no intervention); Group 2. Fluo ridated toothpaste; Group 3. Tooth mousse containing casein phosphopeptide-amorphous calcium phosphate (CPP-ACP); Group 4. Toothpaste containing bio-active glass and fluoride; Group 5. Tooth cream containing nano-hydroxyapatite, fluoride, and xylitol.EMH was evaluated in fifty specimens (each group, n = 10) at three times: before and after de-mineralization, and after 28 days of pH-cycling and treatment.Two samples from each group were observed with SEM.Data were analyzed with multiple-sample repeated measures ANOVA and Tukey's HSD post-hoc tests (p < 0.05).Results: There was a significant difference in EMH between mean baseline values and de-mineralized enamel (p < 0.001).Re-mineralization significantly increased EMH in all treatment groups (p < 0.001).In group 5, the percent of EMH recovery was significantly greater than in other groups (p < 0.001).There were significant differences among the other three treatment groups (all p < 0.001).SEM images showed minerals deposited on the dissolved prismatic enamel in all groups.Conclusions: The nano-hydroxyapatite-based tooth cream was the most effective home oral care product for re-mineralization of primary tooth enamel.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".