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Record W2889716197 · doi:10.1186/s40575-018-0064-x

Labrador retrievers under primary veterinary care in the UK: demography, mortality and disorders

2018· article· en· W2889716197 on OpenAlex
Paul McGreevy, Bethany Wilson, Caroline Mansfield, Dave C. Brodbelt, David B. Church, Navneet K. Dhand, Ricardo J. Soares Magalhães, Dan G. O’Neill

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanine Genetics and Epidemiology · 2018
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
FundersDogs Trust
KeywordsPrimary careDemographyGeographyVeterinary medicineMedicineSocioeconomicsFamily medicineSociology

Abstract

fetched live from OpenAlex

Labrador retrievers are reportedly predisposed to many disorders but accurate prevalence information relating to the general population are lacking. This study aimed to describe demography, mortality and commonly recorded diseases in Labrador retrievers under UK veterinary care. The VetCompass™ programme collects electronic patient record data on dogs attending UK primary-care veterinary practices. Demographic analysis covered all33,320 Labrador retrievers in the VetCompass™ database under veterinary care during 2013 while disorder and mortality data were extracted from a random sample of 2074 (6.2%) of these dogs. Of the Labrador retrievers with information available, 15,427 (46.4%) were female and 15,252 (53.6%) were male. Females were more likely to be neutered than males (59.7% versus 54.8%, P < 0.001). The overall mean adult bodyweight was 33.0 kg (SD 6.1). Adult males were heavier (35.2 kg, SD 5.9 kg) than adult females (30.4 kg, SD 5.2 kg) ( P < 0.001). The median longevity of Labrador retrievers overall was 12.0 years (IQR 9.9–13.8, range 0.0–16.0). The most common recorded colours were black (44.6%), yellow (27.8%) and liver/chocolate (reported from hereon as chocolate) (23.8%). The median longevity of non-chocolate coloured dogs ( n = 139, 12.1 years, IQR 10.2–13.9, range 0.0–16.0) was longer than for chocolate coloured animals ( n = 34, 10.7 years, IQR 9.0–12.4, range 3.8–15.5) ( P = 0.028). Of a random sample of 2074 (6.2%) Labrador retrievers under care in 2013 that had full disorder data extracted, 1277 (61.6%) had at least one disorder recorded. The total number of dogs who died at any date during the study was 176. The most prevalent disorders recorded were otitis externa ( n = 215, prevalence 10.4%, 95% CI: 9.1–11.8), overweight/obesity (183, 8.8%, 95% CI: 7.6–10.1) and degenerative joint disease (115, 5.5%, 95% CI: 4.6–6.6). Overweight/obesity was not statistically significantly associated with neutering in females (8.3% of entire versus 12.5% of neutered, P = 0.065) but was associated with neutering in males (4.1% of entire versus 11.4% of neutered, P < 0.001). The prevalence of otitis externa in black dogs was 12.8%, in yellow dogs it was 17.0% but, in chocolate dogs, it rose to 23.4% (P < 0.001). Similarly, the prevalence of pyo-traumatic dermatitis in black dogs was 1.1%, in yellow dogs it was 1.6% but in chocolate dogs it rose to 4.0% ( P = 0.011). The current study assists prioritisation of health issues within Labrador retrievers. The most common disorders were overweight/obesity, otitis externa and degenerative joint disease. Males were significantly heavier females. These results can alert prospective owners to potential health issues and inform breed-specific wellness checks.

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.

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)0.001
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 score0.012
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.138
GPT teacher head0.378
Teacher spread0.240 · 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