Effects of chitosan on dental bone repair
Bibliographic record
Abstract
Objectives: Bone defects following tumor resection and osteolysis due to dental and bone lesions and periodentium tissue disorders are serious challenges. One of these materials used is chitosan, a derivative of crustaceans’ exoskeleton. The aim of this study was to assess effects of chitosan on socket repair after dental extraction. Methods: Twenty four dental sockets of 15-24 years old patients were visited by a maxillofacial surgeon for extracting premolar teeth for orthodontic purposes. The sockets in one side were filled-in by chitosan. In the other side, the sockets were left unfilled. After 10 weeks, periapical radiographs were obtained from the repair sites, were digitalized and then evaluated for densitometry using Adobe Photoshop Software. Each socket was divided into coronal, middle and apical. Dental density of each socket in case and control groups was recorded. The density of regenerated bone was compared against the maximum bone density of each individual. Wilcoxon signed range test and paired t-test were used for data analysis. Results: Bone density in middle and apical sections in case group was significantly more than control group. In apical section in case group regenerated bone reached up to 98.2% of normal bone density. In each patient, the bone density in epical and middle sections was increase 29.3% and 10.8% of normal bone density. Conclusions: Chitosan significantly increased bone density in epical and middle sections. Chitosan can be used for bone repair in cases of bone loss. Various densitometry studies for evaluating chitosan effects in different bone defects are suggested.
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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) | 0.002 | 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".