Effect of Tooth Bleach on Dentin Fatigue Resistance in Situ
Bibliographic record
Abstract
BACKGROUND: Negative effects of bleaching on dentin have previously been reported in vitro. OBJECTIVE: The purpose of this study was to determine the effect of carbamide peroxide bleaching on dentin fatigue resistance using a clinically relevant in situ model. METHODS AND MATERIALS: Following research ethics board approval, 60 human teeth requiring extraction were collected. Sterilized human dentin specimens were cut (1.2x1.2x10 mm) and secured into customized bleaching trays to be used by study participants. Participants were randomly assigned to either bleach (10% carbamide peroxide, n=23) or control (gel without bleach, n=26) treatment groups. Treatment was applied to the bleaching trays and worn overnight by participants for 14 days. After treatment completion, dentin specimens were removed from the bleaching trays and subjected to fatigue testing (10 N, 3 mm/s, 2x105 cycles) while submerged in artificial saliva. Kaplan-Meier survival analysis was conducted to compare the number of cycles to failure during fatigue testing in both groups. A log rank test was run to determine if there were differences in the survival distribution between the two groups (α<0.05). RESULTS: The median number of cycles to failure was 352 ± 202 and 760 ± 644 for the bleach and control groups, respectively. The survival distributions for the two groups were significantly different (p=0.020). Dentin fatigue resistance was significantly lower in the bleach group compared to the control. CONCLUSIONS: Direct bleaching of human dentin using an at-home tray bleaching protocol in situ reduced dentin fatigue resistance. This has implications for tooth fracture risk and longevity.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".