Whitening Efficacy of Chewing Gum Containing Sodium Metaphosphate on Coffee Stain: Placebo-controlled, Double-blind In Situ Examination
Bibliographic record
Abstract
SUMMARY This study aimed to evaluate the ability of chewing gum containing sodium metaphosphate (SMP) to remove coffee stains from enamel in situ. This was a double-blind (subjects, evaluators), parallel-group, crossover, randomized clinical trial with 30 healthy adult volunteers. Each participant held an appliance with a hydroxyapatite (HA) pellet on the lower lingual side of his or her mouth for two hours to allow pellicle formation. The appliances were subsequently immersed in coffee solution at 37°C for 48 hours. The color of the HA pellet before and after coffee immersion was measured using a spectrophotometer. The participant set the appliance and chewed two pieces of test gum, which contained 7.5 mg of SMP per piece, or control gum without SMP. Each cycle included five minutes of exposure to chewing gum, after which the appliances were placed in 100% relative humidity at room temperature for a 30-minute incubation. This cycle was repeated five times for each gum type. The color of the HA pellet was measured after each chewing cycle using the spectrophotometer. In addition, ΔE* values, which indicate the change in pellet color after each chewing cycle compared with after coffee immersion, were calculated. Data were analyzed using the paired t-test with Bonferroni adjustment to compare ΔE* values of control and test gum after each chewing cycle. The ΔE* values of test gum were significantly higher than those of control gum after all chewing cycles, excluding the first cycle (p<0.05). This finding indicates that test gum containing SMP was more effective at removing coffee stains from the HA pellet than control gum. We conclude that chewing gum containing SMP can effectively remove coffee stains from HA pellets. Thus, SMP is a promising agent to be further explored in tooth-cleaning studies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".