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Record W4226055791 · doi:10.1186/s12917-022-03226-3

Factors contributing to the swimmer puppy syndrome found in Labrador retrievers

2022· article· en· W4226055791 on OpenAlexaboutno aff

Bibliographic record

VenueBMC Veterinary Research · 2022
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsnot available
Fundersnot available
KeywordsPuppyLabrador RetrieverLitterBody weightEpidemiology

Abstract

fetched live from OpenAlex

Abstract Background Swimmer puppy syndrome is a disease found in neonatal puppies mainly characterized by the inability to stand, but its direct cause is unknown. Since swimmer puppies were observed infrequently but continuously among the Labrador retriever colony at the Hokkaido Guide Dogs for the Blind Association in Japan, based on their birth record and pedigree, factors related to the onset of swimmer puppy syndrome in Labrador retrievers were examined. Results The total number of offspring over seven years was 436, of which 16 were swimmer puppies. Most of the affected puppies except one recovered steadily. As for the swimmer puppies, the litter size was significantly lower, and the body weights on the 10th and 28th day after delivery were significantly higher than the non-symptomatic puppies. These results suggested that the onset may be related to weight gain in the neonatal stages due to a small litter size. According to the genetic analysis, 26 ancestors common to the affected individuals were confirmed, but the causative individual could not be identified with the inbreeding coefficient. The heritability of the swimmer-puppy onset trait was 0.80, and the heritability for the the 10 th -day body-weight trait was equally high at 0.78, both of which strongly suggest genetic involvement. Conclusions In this study, the onset of swimmer puppy syndrome in the Labrador retrievers was associated with litter size and early weight gain, and result of study suggests that genetic influence might be involved.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.004
Research integrity0.0000.002
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 head0.443
GPT teacher head0.457
Teacher spread0.014 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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