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Inheritance of Occipital Bone Hypoplasia (Chiari Type I Malformation) in Cavalier King Charles Spaniels

2004· article· en· W4241556105 on OpenAlexfundno aff
Clare Rusbridge, Susan P. Knowler

Bibliographic record

VenueJournal of Veterinary Internal Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicSpinal Dysraphism and Malformations
Canadian institutionsnot available
FundersHospital for Sick Children
KeywordsMedicineHypoplasiaForamen magnumSyringomyeliaAnatomyOccipital boneConsanguinityPediatricsSkullMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

Abstract Occipital bone hypoplasia with foramen magnum obstruction and secondary syringomyelia (SM) is a common condition in the Cavalier King Charles Spaniel (CKCS) that is similar to human Chiari type I malformation. A worldwide family tree of more than 5,500 CKCSs spanning a maximum of 24 generations was established by obtaining pedigree information from 120 dogs diagnosed with SM secondary to occipital bone hypoplasia. The ongoing study showed 6 of 8 great grandparents of all affected dogs could be traced back to 2 female ancestors so that all 8 were descended from one or the other or both. The disease appears to be more severe and have an earlier onset with increased inbreeding, especially when breeding from affected dogs. The family tree of idiopathic epilepsy (IE) appears to be a different subset of the CKCS population, although some overlap was observed. Idiopathic epilepsy is more frequent in lines originating from whole-color dogs. Selection for coat color is believed to have influenced the development of both occipital hypoplasia with secondary SM and IE. In addition, breeding guidelines to reduce the incidence of mitral valve disease have placed further pressures on the gene pool.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.315
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations100
Published2004
Admission routes1
Has abstractyes

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