Penetration of chlorhexidine coating into tooth enamel: A surface analytical study
Bibliographic record
Abstract
Chlorhexidine has proved an efficient antibacterial agent and has been used successfully to prevent new carious lesions in the teeth of adults and children. The substantivity of chlorhexidine has not been identified with any precision, but is certainly not of short duration. In this work, surface analytical techniques have been applied to study the chemical composition, distribution, and penetration of an applied liquid coating containing chlorhexidine onto tooth enamel in order to ascertain mechanisms by which chlorhexidine keeps its long term substantivity. Several hypotheses have been put forward with regard to its substantivity, including concepts of chlorhexidine remaining as a reservoir upon application either in the epithelial surfaces, the tooth surface, or the biofilm. Alternatively, it has been proposed the teeth themselves act as the reservoir. To study this, a chlorhexidine containing liquid coating was applied to the surface of teeth. These were subsequently transversely cross-sectioned. X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry (ToF-SIMS) were performed on both surfaces to ascertain chemical composition and distribution of the applied coating. It was found that it formed a coating layer of about 25 μm thick. High spatial ToF-SIMS images showed little evidence of substantial diffusion of chlorhexidine into the enamel, either from the surface or via the enamel lamellae.
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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 | 0.000 | 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) | 0.001 | 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".