An In Vitro Study: Does Adding Iodine Potassium Iodide and Cetrimide to Calcium Hydroxide Paste Enhance Its Antimicrobial Effect Against Oral Biofilms?
Bibliographic record
Abstract
Objectives This study aimed to evaluate the antibiofilm effect of calcium hydroxide (CH), 0.5% iodine potassium iodide (IKI), and 0.5% cetrimide (CTR), alone and in combinations on one-week and three-week-old biofilms. Materials and methods Gingival plaque was collected, and biofilms were grown in vitro anaerobically. Biofilms were exposed to each of the three medicaments and their combinations for one day, one week, and two weeks. Proportions of dead and live bacteria in the biofilms were evaluated. Results The killing of bacteria by different medicaments in the three-week-old biofilm was lower than in the one-week-old biofilm (p<0.05). The efficacy of IKI and CTR in killing bacteria was weaker than that of CH, but the highest efficacy in killing was achieved when all three were combined (p<0.05). There was no significant difference in the antibiofilm effect between a day's exposure to the mixture of the three medicaments and one or two weeks of treatment with CH alone (p>0.05). Conclusions Three-week-old biofilms are more resistant to medicaments than one-week-old biofilms. Combining IKI and CTR with CH resulted in a stronger antibiofilm effect than using CH alone. Mixing the three medicaments may enable obtaining the desired clinical effect in a shorter exposure time.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".