Computed tomographic findings of dental disease in domestic rabbits (Oryctolagus cuniculus): 100 cases (2009–2017)
Bibliographic record
Abstract
OBJECTIVE: To characterize the CT findings and epidemiological features of acquired dental disease in rabbits. ANIMALS: ). PROCEDURES: Medical records were searched to identify rabbits that underwent skull CT for any reason from 2009 to 2017. History, signalment, and physical examination findings were recorded. The CT images were reevaluated retrospectively for evidence of dental disease and graded according to a previously described system (from 1 [no evidence of disease] to 5 [severe dental disease]) for acquired dental disease in rabbits, and an overall (mean) grade was assigned. Descriptive analyses were performed. Factors were assessed for associations between dental disease grade and malocclusion stage. RESULTS: Common findings included premolar or molar tooth curvature in transverse (n = 100 rabbits) and sagittal (95) planes, apical elongation of premolar or molar teeth (99), sharp dental points (93), deformation of the mandibular canal (82), and periodontal ligament space widening (76). Acquired dental disease was classified as grade 1 (n = 2 rabbits), 2 (60), 3 (14), 4 (4), or 5 (20). Most CT findings were significantly correlated with each other. Agreement of grades was fair between left- and right-sided quadrants and between maxillary and mandibular quadrants. Age was associated with increasing dental disease grade and malocclusion stage (proportional ORs, 1.21 and 1.32/y, respectively). CONCLUSIONS AND CLINICAL RELEVANCE: Fair agreement in disease grades between dental quadrant pairs indicated a degree of asynchrony in the development of dental disease. Findings suggested premolar or molar tooth curvature in a sagittal plane, subtle elongation at premolar or molar tooth apices, and mandibular canal deformation should be added to the grading system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".