Association of the <i>FGF4L2</i> retrogene with fibrocartilaginous embolic myelopathy in dogs
Bibliographic record
Abstract
BACKGROUND: Fibrocartilaginous embolic myelopathy (FCE) is a well-documented condition in dogs although rarely reported in chondrodystrophic breeds. Genetic associations have not been defined. OBJECTIVES: Define the association of the chondrodystrophy-associated FGF4L2 retrogene with histopathologically confirmed cases of FCE. ANIMALS: Ninety-eight dogs with a histopathologic diagnosis of FCE. METHODS: Retrospective multicenter study. Dogs were genotyped for the FGF4L2 and FGF4L1 retrogenes using DNA extracted from formalin-fixed, paraffin-embedded tissue. Associations between breed, FCE and retrogene status were investigated with reference to a hospital population and known breed and general population allele frequencies. RESULTS: FGF4L2 genotype was defined in 89 FCE cases. Fibrocartilaginous embolic myelopathy was present in 22 dogs from FGF4L2-segregating breeds with allele frequencies of ≥5%; however, all dogs were wild type. Two Labrador retrievers with FCE carried FGF4L2 alleles. Frequency of the FGF4L2 allele was significantly (P < .001) and negatively associated with FCE relative to predicted hospital-population dogs. FCE was overrepresented in Boxer, Great Dane, Yorkshire Terrier, Bernese Mountain Dog, Miniature Schnauzer, Rottweiler, and Shetland Sheepdog breeds. CONCLUSIONS AND CLINICAL IMPORTANCE: Study data based on genotypically and histopathologically defined cases support the historical observation that FCE is uncommon in chondrodystrophic dog breeds. FGF4 plays an important role in angiogenesis and vascular integrity; anatomical studies comparing chondrodystrophic and non-chondrodystrophic dogs might provide insight into the pathogenesis of FCE.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".