In vitro tooth whitening effect of two medicated chewing gums compared to a whitening gum and saliva
Bibliographic record
Abstract
BACKGROUND: Extrinsic staining of teeth may result from the deposition of a variety of pigments into or onto the tooth surface, which originate mainly from diet or from tobacco use. More recently, clinical studies have demonstrated the efficacy of some chewing gums in removing extrinsic tooth staining. The aim of this study was to assess the effectiveness of two nicotine medicated chewing gums (A and B) on stain removal in an in vitro experiment, when compared with a confectionary whitening chewing gum (C) and human saliva (D). METHODS: Bovine incisors were stained by alternating air exposure and immersion in a broth containing natural pigments such as coffee, tea and oral microorganisms for 10 days. Stained enamel samples were exposed to saliva alone or to the test chewing gums under conditions simulating human mastication. The coloration change of the enamel samples was measured using a spectrophotometer. Measurements were obtained for each specimen (average of three absorbances) using the L*a*b scale: lightness (L*), red-green (a) and yellow-blue (b). RESULTS: Medicated chewing gums (A and B) removed a greater amount of visible extrinsic stain, while the confectionary chewing gum with a whitening claim (C) had a milder whitening effect as evaluated by quantitative and qualitative assessment. CONCLUSION: The tested Nicotine Replacement Therapy (NRT) chewing gums were more effective in the removal of the extrinsic tooth stain. This visible improvement in tooth whitening appearance could strengthen the smokers' motivation to quit smoking.
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 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 | 0.001 |
| 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) | 0.001 | 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".