Effects of prolonged use of over‐the‐counter bleaching agents on enamel: An in vitro study
Bibliographic record
Abstract
Abstract This study evaluated the effects of four over‐the‐counter (OTC) bleaching products on the properties of enamel. Extracted human molars were randomly assigned into four groups ( n = 5): PD: Poladay (SDI), WG: White Teeth Global (White Teeth Global), CW: Crest3DWhite (Procter & Gamble), and HS: HiSmile (HiSmile). The hydrogen peroxide (H 2 O 2 ) content in each product was analyzed via titration. Twenty teeth were sectioned into quarters, embedded in epoxy resin, and polished. Each quarter‐tooth surface was treated with one of the four beaching times: T0: control/no‐bleaching, T14: 14 days, T28: 28 days, and T56: 56 days. Materials were applied to enamel surfaces as recommended. Enamel surfaces were examined for ultramicrohardness (UMH), elastic modulus (EM), superficial roughness (Sa), and scanning electron microscopy (SEM). Ten additional teeth were used to evaluate color and degree of demineralization (DD) ( n = 5). Data were statistically tested by two‐way ANOVA and Tukey's tests ( α = 5%). Enamel surfaces treated with PD and WG presented UMH values significantly lower than the controls ( p < .05). Elastic modulus ( E ) was significantly reduced at T14 and T28 for PD, and at T14 for HS ( p < .05). A significant increase in Sa was observed for CW at T14 ( p < .05). Color changes were observed in the PD and WG groups. Additionally, DD analysis showed significant demineralization at T56 for CW. Overall, more evident morphological alterations were observed for bleaching products with higher concentrations of H 2 O 2 ( p < .05), PD, and WG. Over‐the‐counter bleaching products containing H 2 O 2 can significantly alter enamel properties, especially when application time is extended.
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 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 | 0.001 | 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) | 0.000 | 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".