Muscular Dystrophy in Dogs
Bibliographic record
Abstract
Golden Retriever (GR) muscular dystrophy is an inherited degenerative muscle disease that provides an excellent model for Duchenne muscular dystrophy in humans. This study defined the histopathologic lesions, including the distribution of type I and II muscle fibers (FTI and FTII), in 12 dystrophic and 3 nondystrophic dogs between 7 and 15 months of age. The authors were interested in studying the influence on disease phenotype from crossing the base GR breed with Yellow Labrador Retrievers. The dystrophic dogs were divided according to breed: GRs and Golden Labrador Retrievers (GLRs). On hematoxylin and eosin staining, histopathologic lesions were more severe in GRs than GLRs. Six of eight GR muscles (75%) had a severe lesion grade (grade 3). In contrast, seven GLR muscles (87.5%) had mild lesions (grade 2), and only one had severe lesions (grade 3). Changes in fiber-type distribution were more pronounced in GRs versus GLRs. FTI:FTII ratio inversion was observed in three dystrophic GRs but only one GLR. The mean diameter of FTI and FTII was smaller in GRs and GLRs than in nondystrophic dogs (P < .01). The FTI of five GR muscles (62.5%) were larger than those of GLRs, whereas only one GLR muscle was larger (P < .05). The differential was less pronounced for FTII, with four GR muscles being larger and three GLR being larger. Observations indicate that crossing the base GR breed with Labrador Retrievers lessened the severity of the GR muscular dystrophy phenotype.
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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 | 0.000 |
| 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) | 0.002 | 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".