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Record W1887271937 · doi:10.1136/vr.102960

Disease patterns and incidence of immune‐mediated disease in insured Swedish Nova Scotia Duck Tolling Retrievers

2015· article· en· W1887271937 on OpenAlexaboutno aff
Hanna Bremer, Åsa Vilson, B. N. Bonnett, Helene Hansson‐Hamlin

Bibliographic record

VenueVeterinary Record · 2015
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
FundersAgria DjurförsäkringSveriges LantbruksuniversitetVetenskapsrådetSvenska Forskningsrådet Formas
KeywordsBreedIncidence (geometry)Nova scotiaVeterinary medicineDiseaseLabrador RetrieverMedicineArteritisBiologyInternal medicineAnimal sciencePathologyGeography

Abstract

fetched live from OpenAlex

In this study, morbidity in insured Nova Scotia Duck Tolling Retriever (NSDTR) dogs from Sweden was investigated and compared with all other breeds and other retriever breeds. In addition to describing common morbidities in NSDTRs, the hypotheses that NSDTRs are predisposed to lymphoma, immune-mediated rheumatic disease (IMRD) and steroid-responsive meningitis-arteritis (SRMA) were tested. Included in the study were 445,336 dogs; of which, 2890 were NSDTRs that had been covered by veterinary insurance from the Agria Insurance Company (Stockholm, Sweden) at some point during the years 1995-2006. Incidences of various health problems were calculated using the number of veterinary visits as the numerator and the exact time at risk as the denominator. Overall, morbidity was higher in NSDTRs compared with all other breeds, but similar compared with other retriever breeds. The most common causes of veterinary visits in NSDTRs were injuries, gastrointestinal disease and locomotor disorders, with NSDTRs at increased risk of these compared with all other breeds. The incidences for IMRD, SRMA and lymphoma were significantly higher in NSDTRs than in all other dog breeds and all other retriever breeds. The study describes morbidity in NSDTRs, and identifies several disorders to which the breed is predisposed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.085
GPT teacher head0.353
Teacher spread0.268 · 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 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

Citations8
Published2015
Admission routes1
Has abstractyes

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