Evaluating the efficacy of desensitizing dentifrices on dentinal hypersensitivity management: A scanning electron microscopic analysis
Bibliographic record
Abstract
INTRODUCTION: Dentin hypersensitivity (DH) adversely affects quality of life by impairing the ability to eat and drink certain foods, therefore, should be addressed promptly with a contingency plan that includes initial treatment with a noninvasive prophylactic home care approach, followed by in-office treatment if desired result is not achieved. AIM: ) based toothpastes in the treatment of DH. MATERIALS AND METHODS: One hundred dentin slices were cut from the crown section of molars. Etching with 37% orthophosphoric acid was done to open the tubules. Scanning electron microscope (SEM) study was done to ensure that the tubules are opened. The dentin specimens were divided into four groups: Group 1 -5% NovaMin, Group 2 - Propolis, Group 3 - 5% potassium nitrate, and Group 4 - 8% arginine. Samples were brushed for 2 min twice daily with a soft toothbrush for 15 days and were stored in distilled water. The samples were rinsed under running water to remove the toothpaste. The SEM was performed to check the obliteration of tubules. RESULTS: ≤ 0.05). CONCLUSION: In the present study, all materials, NovaMin, Propolis, potassium nitrate, and arginine, were effective in occluding dentinal tubules but NovaMin appeared most proficient in occluding tubules, followed by arginine, potassium nitrate, and Propolis. Topical NovaMin is an upcoming agent demonstrating desensitization and remineralization properties.
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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 | 0.001 | 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) | 0.002 | 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".