Effects of Catalyst Bioproducts on In-Office Whitening: A Randomized, Split-mouth Clinical Analysis
Bibliographic record
Abstract
OBJECTIVES: To compare conventional hydrogen peroxide (H2O2) whitening therapy (CT) applied for 45 minutes with a test therapy (TT) containing H2O2 whitening gel and catalyst bioproducts applied for 15 minutes. METHODS: Thirty patients and their hemiarches were randomly divided into two groups: CT: application of 35% H2O2 three times for 15 minutes; test therapy (TT): based on the prior application of a polycaprolactone scaffold and the addition of 10 mg of peroxidase to the 35% whitening gel (3 drops of thickener, 9 drops of peroxide and 10 mg of peroxidase) for an exposure time of 15 minutes.The two treatments were carried out in three whitening sessions, 7 days apart. The chromatic change (ΔE00) and the bleaching index (ΔWID) were analyzed by spectrophotometry. Spontaneous sensitivity was assessed through a questionnaire, and thermal sensitivity was provoked through thermal stimuli after the three sessions and 14 days later. Esthetic self-perception was also measured using the Orofacial Esthetics Scale before and after each session. RESULTS: After the first session, CT exceeded TT in ΔE00 and ΔWID, whereas they were equal at the other time points. Greater intensity and occurrence of spontaneous sensitivity occurred in the first and second sessions with CT. The CT group experienced thermal sensitivity at higher temperatures than the TT group at all times analyzed. Esthetic self-perception was higher (66.6%) in the TT group. CONCLUSION: The test therapy can achieve the same whitening effect with less total exposure time and less tooth sensitivity than the conventional technique.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".