Prevention of tea‐induced extrinsic tooth stain
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to determine whether the addition of milk to tea reduces the ability of tea to stain extracted human teeth and, if so, to ascertain the component of milk that is responsible for milk's stain reducing properties. METHODS: Extracted human teeth were immersed in a tea solution, with the addition of 2% milk, 5.26% lactose, 2.7% casein or 10% fat-free milk for 24 h at 37°C. A dental spectrophotometer (VITA Easyshade Compact) was used to evaluate the colour of the teeth both before and after immersion in the tea solutions. Commission internationale de l'éclairage (CIE) L*a*b* colour space values were recorded, and the change in colour (ΔE*) was calculated. A two-tailed t-test or one-way analysis of variance (anova) was used to determine whether there were statistical differences between groups. RESULTS: Milk significantly reduces the ability of tea to stain teeth (P = 0.0225), specifically in the L* and a* dimensions (P = 0.0182 and P = 0.0124, respectively) of the colour sphere. Casein, which makes up 80% of the protein content in bovine milk, is the component of milk that is responsible for significantly reducing tea's ability to stain teeth (P < 0.0001). CONCLUSIONS: The addition of milk to tea significantly reduces the tea's ability to stain teeth. Casein was determined to be the component of milk that is responsible for preventing tea-induced staining of teeth to a similar order of magnitude that can be obtained by vital bleaching treatments.
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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) | 0.000 | 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".