Role of Chitosan Hydrogels in Clinical Dentistry
Bibliographic record
Abstract
Biopolymers are organic polymers that can be treated into intricate designs with porous characteristics that mimic essential biologic components. Due to their superior biosafety, biodegradability, biocompatibility, etc., they have been utilized immensely in biomedical engineering, regeneration, and drug delivery. To obtain the greatest number of results, a literature search was undertaken in scientific search engines utilizing keywords. Chitosan is used in a variety of medical sectors, with the goal of emphasizing its applications and benefits in the clinical dental industry. Chitosan can be dissolved in liquid form and combined with other substances to create a variety of products, including fibers, hydrogels, membranes, microspheres, resins, sponges, pastes, tablets, and micro granules. Chitosan has been studied in a variety of dental applications. Chitosan is used in the prevention of caries and wear, in pulpotomy to accelerate osteogenesis in guided tissue regeneration due to its hemostatic property, and primarily to benefit from its antimicrobial activity by adding it to materials, such as glass ionomer cement, calcium hydroxide, and adhesive systems. With its antibacterial activity and biocompatibility, chitosan is leading the pack as a promising ingredient in the production of dental materials. The current review provides an update on the background, fundamentals, and wide range of uses of chitosan and its gels in dental science.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".