Evaluation of a model for induction of periodontal disease in dogs
Bibliographic record
Abstract
There are several methods for inducing periodontal disease in animal models, being the bone defect one of the most reported. This study aimed to evaluate this model, through clinical, radiographic, tomographic and histological analyzes, thus providing standardized data for future regenerative works. Twelve dogs were subjected to the induction protocol. In a first surgical procedure, a mucoperiosteal flap was made on the buccal aspect of the right third and fourth premolars and a defect was produced exposing the furcation and mesial and distal roots, with dimensions: 5mm coronoapical, 5mm mesiodistal, and 3mm buccolingual. Periodontal ligament and cementum were curetted and the defect was filled with molding polyester, which was removed after 21 days on new surgical procedure. Clinical and radiographic examinations were performed after the two surgeries and before the collection of parts for dental tomography and histological analysis. All animals showed grade II furcation exposure in both teeth. Clinical attachment level increased after induction. Defect size did not change for coronoapical and buccolingual measurements, while mesiodistal size was significantly higher than at the time of defect production. Radiographic analysis showed decreased radiopacity and discontinuity of lamina dura in every tooth in the furcation area. The horizontal progression of the disease was evident in micro-computed tomography and defect content in the histological analysis. Therefore, it is concluded that this method promotes the induction of periodontal disease in dogs in a standardized way, thus being a good model for future work.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".