Frequency of the Allelic Variant of the <i>PTPLA</i> Gene Responsible for Centronuclear Myopathy in Labrador Retriever Dogs as Assessed in Italy
Bibliographic record
Abstract
Centronuclear myopathy (CNM) is an autosomal recessive hereditary disease affecting Labrador Retriever dogs. The disease is characterized by muscle lesions, typically encompassing reduction in the number and atrophy of type II fibers, and is caused by a short interspersed repeat element insertion in exon 2 of the protein tyrosine phosphatase-like member A. The actual allele frequency is unknown; a study was undertaken to ascertain it using a convenience-sample population composed of 217 Labrador Retrievers. In addition to 3 subjects already diagnosed with CNM, used as positive controls for polymerase chain reaction, only 2 unrelated dogs were heterozygous wild-type/mutation (wild-type/mut). Thus, the frequency of the CNM allele observed in the present study was 1.8% and 0.47% when including and excluding the 3 mut/mut homozygous cases, respectively. Based on the Hardy-Weinberg exact test (P = 1.00), the genotype frequency without the CNM-affected dogs was in agreement with the Hardy-Weinberg equilibrium. Assuming the Hardy-Weinberg equilibrium law, the expected frequency of the homozygous mutated genotype was calculated to be approximately 0.00005, which corresponds to 1 case of CNM out of 20,000 dogs. In conclusion, the present study indicates that the CNM allele is present but rare in a convenience sample of Labrador Retrievers in Italy.
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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 | 0.000 |
| 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".