Periodontal diseases and other dental disorders in dogs : An epidemiologic study
Bibliographic record
Abstract
The study determined the epidemiology of periodontal diseases among dogs. This study was conducted on 181 dogs older than 6 months to examine their oral cavities and gather information about their feeding habits. Periodontal diseases were reported in 59.67% of dogs. It was highest for Spitz (75.61%), followed by German shepherd (64.49%), Mongrel (61.53%), Labrador (51.02%) and other Non-descriptive breeds (33.9%). Compared to dogs fed only vegetarian diets, those fed non-vegetarian diets had few health problems. There was the highest incidence of dental problems among vegetarians (69.28%), followed by those given a combination of vegetarian and non-vegetarian diets (51.32%). Periodontitis was common in these dogs, regardless of its cause, and its incidence increased with age. Lesions were more severe in the premolar and molar regions than in the maxillary and mandibular incisor regions. The incidence of missing teeth increased with age. First premolars were the most commonly lost teeth, followed by other premolars and molars, where severe periodontitis was commonly found. The incidence and severity of calculus on teeth increased with age. Due to these findings, it is especially important to keep dogs’ dental hygiene in good condition and conduct continuous periodic examinations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".