Subvalvular Aortic Stenosis: Learning From Human and Canine Clinical Research
Bibliographic record
Abstract
Subvalvular aortic stenosis (SAS) is the most common congenital heart disease (CHD) in dogs and is also prevalent in human children. A fibrous ridge below the aortic valve narrows the left ventricular outflow tract (LVOT) and increases blood flow velocity, leading to devastating side effects in diseased patients. Due to the similarities in presentation, anatomy, pathophysiology, cardiac development, genomics, and environment between humans and dogs, canine SAS patients represent a critical translational model of human SAS. Potential adverse outcomes of SAS include arrhythmias, left-sided congestive heart failure, endocarditis, exercise intolerance, syncope, and sudden cardiac death. The greatest divergence between canine and human SAS clinical research has been the standard of care regarding treatment of these outcomes, with pharmacological intervention dominating best practices in veterinary medicine and surgical intervention comprising the standard practice for human SAS patients. Regardless of the species, the field has yet to identify a treatment option to prevent disease progression or permanently remove the fibrous ridge, but historical leaps in SAS research support a continued translational approach as the most promising method for achieving this goal.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".