Anti‐desmoglein‐2 autoantibodies do not discriminate between UK boxer dogs with and without arrhythmogenic right ventricular cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Evidence regarding the diagnostic utility of serum anti-desmoglein-2 (DSG2) autoantibodies for arrhythmogenic right ventricular cardiomyopathy (ARVC) in boxer dogs is conflicting. METHODS: Prospective standardised evaluation of apparently healthy boxer dogs for ARVC was performed at three referral centres, including blood pressure measurement, electrocardiography, echocardiography, haematology, biochemistry (including cardiac troponin I) and 24-hour Holter monitoring. Additional dogs with a diagnosis of ARVC were retrospectively recruited. ARVC disease status was defined using cut-offs of 20 or less (unaffected) and more than 300 (affected) ventricular premature complexes of right ventricular origin in 24 hours. The residual serum samples were stored at ‒80°C for analysis for anti-DSG2 autoantibodies using ELISA techniques. RESULTS: Forty boxer dogs were enrolled (11 healthy controls, 10 with preclinical ARVC and 19 with clinical ARVC). Serum anti-DSG2 autoantibodies were detected in all dogs, bar one healthy dog. DSG2 differed significantly between groups (p = 0.031) and was significantly lower in dogs with preclinical versus clinical ARVC (p = 0.025). LIMITATIONS: Some data were collected retrospectively, and some dogs were receiving antiarrhythmic therapy. CONCLUSION: Serum DSG2 autoantibodies can be present in boxer dogs with preclinical and clinical ARVC and apparently healthy controls.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".