Evaluation of the genetic basis of tricuspid valve dysplasia in Labrador Retrievers
Bibliographic record
Abstract
OBJECTIVE: To quantify inheritance of tricuspid valve dysplasia (TVD) in a population of Labrador Retrievers and evaluate the possibility of the effect of a major locus on TVD. ANIMALS: 521 Labrador Retrievers (345 with known phenotypes and 176 related dogs with unknown phenotypes). PROCEDURES: Dogs were considered normal, equivocal, and affected for TVD on the basis of echocardiographic appearance of the tricuspid valves. Information on related dogs was collected for estimation of heritability of the 3 categories of phenotype, using a threshold model. Complex segregation analysis was performed to evaluate the possibility of the effect of a major locus on TVD. RESULTS: Heritability of TVD in this population of dogs was found to be 0.71, a value sufficiently large to suggest a segregating major locus. Subsequent complex segregation analysis did not provide sufficiently strong evidence to indicate influence of a major locus on the prevalence of TVD. However, complex segregation analysis for 2 categories of phenotype (eg, equivocal dogs were grouped with affected dogs) suggested that there was a single recessive allele with a substantial impact on the expression of TVD. CONCLUSIONS AND CLINICAL RELEVANCE: In Labrador Retrievers, TVD is a heritable disorder. Affected dogs and dogs closely related to affected dogs should not be used for breeding. There was insufficient evidence to suggest the influence of a major locus on TVD, although this conclusion was affected by the classification of dogs for diagnosis of the condition.
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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 | 0.001 | 0.002 |
| 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".