New findings about releasing of chloride ionsand quaternary ammonium compounds fromconventional and experimental glass ionomers
Bibliographic record
Abstract
Introduction: Glass ionomer cements (GICs), the most frequently used restorative materials in pediatric dentistry, may be used as medium for slow release of other active anti-microbial components apart from fluoride.Objectives: The objectives of this study were to determine the level of released chloride ions and anti-microbial compounds by incorporating 2% anti-microbial agents into conventional and experimental GICs. Material and methods:The study was carried out using a conventional glass ionomer cements ChemFlex and Fuji IX as well as experimental glass ionomer cement MP4 and anti-microbial compounds, such as cetylpyridinium chloride and benzalkonium chloride.A total of 36 specimens (4 mm × 6 mm) were prepared, including 6 specimens of each GIC integrated with 2% of anti-microbial agents, and other six samples of the same cement without any anti-microbial agents used as a control group.Amounts of released Cl-ions were determined with the use of an ion-selective electrode, and amounts of anti-microbial agents were determined by UV/VIS spectrophotometer.Measurements were performed at 9 successive time intervals starting from fifteenth minute and finishing after seven days. Results:The results obtained show a continual release of both chloride ions and anti-microbial compounds from analyzed GICs that increased over time.Conclusions: In comparison with conventional GICs, MP4 GIC produces a significantly higher release both of chloride ions and anti-microbial compounds.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".