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Record W2908549362 · doi:10.1136/vr.105135

Pilot study of head conformation changes over time in the Cavalier King Charles spaniel breed

2019· article· en· W2908549362 on OpenAlexaff
Susan P. Knowler, Lena Gillstedt, Thomas J. Mitchell, Jelena Jovanovik, Holger A. Volk, Clare Rusbridge

Bibliographic record

VenueVeterinary Record · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsCollege of the Rockies
FundersSvenska Kennelklubben
KeywordsBreedKnightHead (geology)Veterinary medicineBiologyAnatomyMedicineZoologyAnimal sciencePhysics

Abstract

fetched live from OpenAlex

Modern interpretation of head conformation in the Cavalier King Charles spaniel (CKCS) has favoured a smaller, more exaggerated, brachycephalic type than originally described in the 1929 breed standard. Recent research studies identified brachycephaly and reduced hind cranium as two conformational (dysmorphic) features that increase risk for symptomatic Chiari-like malformation and secondary syringomyelia (SM). A prospective pilot study investigated the hypothesis that dysmorphic head features could be assessed visually and correlated with risk of SM. Thirteen CKCS, selected from anonymised photographic evidence, were physically appraised by authorised Kennel Club judges using a head shape checklist. These subjective evaluations were then matched with objective measurements of the cranium (cephalic index and rostrocaudal doming) and their subsequent MRI. A positive correlation (P=0.039) between the judges' checklist score and rostrocaudal doming (hindskull ratio) and a positive correlation between the cephalic index and hindskull ratio (P=0.042) were identified. Five CKCS had no SM and their status tallied with 62 per cent of the judges' evaluation. Although the ability of adjudicators to identify differences in head conformation varied, there was sufficient association between the dysmorphic parameters and the risk of SM to cause concern and propose a larger study in CKCS breed.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.318

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 head0.037
GPT teacher head0.274
Teacher spread0.236 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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