Effect of two desensitizing agents on dentin hypersensitivity: A randomized split-mouth clinical trial
Bibliographic record
Abstract
Background: Clinical research is important to evaluate the effect of desensitizing agents. Aims: This randomized clinical trial evaluated the immediate and 1 week desensitizing effect of two desensitizing agents Uno Topical Gel and Profluorid. Materials and Methods: Thirtyfive patients with teeth presenting with dentin hypersensitivity were included in this clinical trial. Each quadrant in a patient was randomly assigned to one of two groups: Uno Topical Gel or Profluorid Varnish. A VAS score was used to assess tooth sensitivity at baseline, immediately after application of desensitizer and after 1 week. Additionally, 30 dentin discs were prepared, divided into Group 1(Control Group), Group 2 (Profluorid Varnish) and Group 3 (Uno Topical Gel) and examined using scanning electron microscopy (SEM) after 1hour and 24 hours to evaluate tubule occlusion. Statistical Analysis: Clinical data were analysed using Friedman's test and Mann – Whitney U test. SEM data was analysed using Student's 2-sample t-test. Results: Uno group was significantly better to evaporative stimuli immediately (P=0.01) after application. After 1 week, Uno group was significantly better to tactile (P=0.000) and evaporative (P=0.000) stimuli than Profluorid. SEM images showed that 1 hour after application, Uno and Profluorid demonstrated more than 90% and 80% dentin tubule occlusion respectively. At 24 hours, Uno and Profluorid demonstrated more than 50% and 60% dentin tubule occlusion respectively. Conclusions: Uno Topical Gel was significantly better than Profluorid in reducing pain of dentin hypersensitivity due to tactile and evaporative stimuli after 1 week.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".