Treatment of white spot lesions with ACP paste and microabrasion
Bibliographic record
Abstract
OBJECTIVE: To examine the effects of application of casein phosphopeptide amorphous calcium phosphate (CPP-ACP) paste and microabrasion treatment on the regression of white spot lesions (WSLs). MATERIALS AND METHODS: Artificially-induced WSLs in bovine enamel were randomly assigned to one of four treatment groups: CPP-ACP paste only, microabrasion only, microabrasion and CPP-ACP, and a control. Samples were treated with each regimen twice daily for 2 weeks and stored in remineralizing solution between the treatments. Quantitative light-induced fluorescence was used to measure changes in fluorescence, which indicate changes in mineral content of WSLs immediately before (T1) and 2 weeks after treatment (T2). A two-within-subject factor analysis of variance was used to analyze the significance of any changes in mineral content of the lesions from T1 to T2. RESULTS: There was a statistically significant (P < .05) gain in fluorescence associated with the microabrasion only, as well as the microabrasion and CPP-ACP treatments. The changes in fluorescence for the CPP-ACP treatment alone were not statistically significant (P = .40). CONCLUSIONS: CPP-ACP paste alone does not significantly improve the fluorescence value (ie, the mineral content) of WSLs. Within the limitations of this in vitro study, microabrasion treatment with or without CPP-ACP improved the fluorescence and thus reduced WSLs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".