Prevalence of Dental and Skeletal Malocclusions in Mesaticephalic and Dolichocephalic Dogs—a Retrospective Study (2015–2018)
Bibliographic record
Abstract
Medical records of dogs with dolico- or mesaticephalic conformation who were presented to a private veterinary referral dental practice with malocclusion of the deciduous or permanent dentition were retrospectively reviewed from a 3-year period (2015-2018). Records were evaluated to determine the type(s) of malocclusions and 198 dogs were evaluated with permanent malocclusions. Of the dogs with deciduous malocclusions, 45 (60%) had variations of a MAL1, 28 (38%) had a MAL2, 13 (17.6%) had a MAL3, and four (5.4%) had a MAL4, with 19 (26%) having more than one type of malocclusion. Poodles, Labrador Retrievers, and Cavalier King Charles Spaniels consisted of 37 (50%) of the dogs with deciduous malocclusions. Fifty-five (74%) dogs proceeded with interceptive orthodontics. Of the dogs with permanent malocclusions, 128 (65%) had a variant of a MAL1, 60 (30%) had a MAL2, 75 (38%) had a MAL3, and 11 (5.6%) had a MAL4, with a MAL1 occurring concurrently with 49 (82%) MAL2 cases. The most common type of MAL1 was linguoversion of one or both mandibular canine teeth in 92 (72%) dogs. The five most commonly affected breeds with permanent malocclusions were Poodles, Labrador Retrievers, Chihuahuas, Pit Bull Terriers, and Cavalier King Charles Spaniels. Overall, 39 (18%) dogs presented with malocclusions observed in this study were associated with the Poodle breed and 20 (9%) dogs were associated with the Labrador Retriever breed. This trend among Poodle mixes and Labrador Retrievers supports a familial pattern to malocclusions.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".