COMPARATIVE ANALYSIS OF ENAMEL SURFACE WEAR INDUCED BY TWO CONCENTRATIONS OF ZIRCONIA PARTICLE TOOTHPASTE UNDER TWO ELECTRIC TOOTHBRUSHING MODALITIES.
Bibliographic record
Abstract
BACKGROUND: Dental enamel, the outermost layer of human teeth, is a mineralized tissue that must endure repetitive forces, wear, and corrosion resulting from everyday oral activities like chewing. Its high mineral content provides the strength and hardness necessary for proper tooth function. However, over time, wear, fractures, or cavities often lead to the need for dental repairs. Additionally, unlike other human tissues, dental enamel lacks significant regenerative or self-repair capabilities, making tooth restorations more essential. OBJECTIVE: To evaluate the effects of two electric toothbrush modalities and two different concentrations of zirconia-containing whitening toothpaste on enamel surface roughness. MATERIALS AND METHODS: Forty extracted lower third molars were divided into four groups based on toothpaste concentration (3% and 4% zirconia) and brushing mode (active and soft). Each tooth was brushed for 5 minutes using an oscillating electric toothbrush. Enamel surface roughness was measured before and after brushing using a contact profilometer. RESULTS: Brushing with 4% zirconia toothpaste in active mode resulted in the highest increase in enamel surface roughness, particularly in Rz and Rq values. Conversely, the soft mode, especially with 4% zirconia toothpaste, demonstrated a protective trend, where post-brushing roughness values decreased in some metrics. However, these changes did not reach statistical significance across any group (p>0.05). CONCLUSION: While higher concentrations of zirconia in whitening toothpaste and increased brushing force may influence enamel surface roughness, the observed changes were not statistically significant. Further research is needed to determine safe concentrations and brushing techniques to minimize enamel wear.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".