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Record W4388268370 · doi:10.14740/cr1547

Subvalvular Aortic Stenosis: Learning From Human and Canine Clinical Research

2023· review· en· W4388268370 on OpenAlexvenueno aff
Amanda E. Crofton, Samantha L. Kovacs, Joshua A. Stern

Bibliographic record

VenueCardiology Research · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCardiologyMedicineInternal medicineStenosis

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.602
GPT teacher head0.657
Teacher spread0.055 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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