Dentinogenesis imperfecta in a 6-year-old male neutered Labrador retriever: Case report with atypical clinical presentation and treatment review
Bibliographic record
Abstract
This case report details the diagnosis and treatment of dentinogenesis imperfecta in a 6-year-old neutered male Labrador, presenting without concurrent osteogenesis imperfecta. Diagnostic modalities, including radiographs, CT imaging, and histopathological examination, are reviewed in conjunction with the latest literature on canine dentinogenesis imperfecta. This patient presented at a more advanced age than typically reported cases. The clinical history, as provided by referring veterinarians, documented fractured deciduous teeth with delayed exfoliation. By 10 months of age, the patient's permanent dentition exhibited a translucent appearance and structural anomalies. Upon presentation to Eastcott Referrals the patient was experiencing significant oral pain and exhibited generalised coronal wear with yellow/brown intrinsic discolouration. CT imaging revealed that all teeth had endodontic disease and associated apical periodontitis, with varied root canal widths indicating that teeth succumbed to endodontic disease at different time points. The treatment protocol involved staged full-mouth extractions, resulting in the complete resolution of clinical symptoms. This case underscores the importance of early diagnosis and intervention in managing dentinogenesis imperfecta in dogs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".