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Record W2114919180 · doi:10.1136/vr.100019

Genetic evidence of subaortic stenosis in the Newfoundland dog

2012· article· en· W2114919180 on OpenAlexaboutno aff
S. B. Reist‐Marti, G. Dolf, Tosso Leeb, S. Kottmann, S. Kietzmann, Karin Butenhoff, Stefan Rieder

Bibliographic record

VenueVeterinary Record · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPenetrancePopulationIncidence (geometry)Inheritance (genetic algorithm)BiologyDemographyMedicineGeneticsZoologyEnvironmental healthGene

Abstract

fetched live from OpenAlex

Subaortic stenosis (SAS) is a cardiac disorder with a narrowing of the descending aorta below the left ventricular outflow tract of the heart. It occurs in several species and breeds. The Newfoundland is one of the dog breeds where it is more common and usually leads to death at early adulthood. It is still discussed to which extent SAS has a genetic background and what its mode of inheritance could be. Extensive pedigree data comprising more than 230,000 Newfoundland dogs from the European and North American population reaching back to the 19th century including 6023 dogs with a SAS diagnosis were analysed for genetic factors influencing SAS affection. The incidence and prevalence of SAS in the analysed Newfoundland population sample were much higher than those reported in previous studies on smaller population samples. Assuming that some SAS-affected dogs remained undiscovered or were not reported, these figures may even be underestimated. SAS-affected Newfoundland dogs were more often inbred and closer related to each other than unaffected dogs, which is an indicator for a genetic background of SAS. The sex had no significant impact on SAS affectedness, pointing at an autosomal inheritance. The only simple mode of inheritance that fitted the data well was autosomal codominant with lethal homozygosity and a penetrance of 1/3 in the heterozygotes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.073
GPT teacher head0.329
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations18
Published2012
Admission routes1
Has abstractyes

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