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Record W2402586644 · doi:10.1136/vetreco-2015-000166

Age‐associated and breed‐associated variations in haematological and biochemical variables in young labrador retriever and miniature schnauzer dogs

2016· article· en· W2402586644 on OpenAlexaboutno aff
Thomas Brenten, Penelope Morris, Carina Salt, Jens Raila, Barbara Kohn, Florian J. Schweigert�, Jürgen Zentek

Bibliographic record

VenueVeterinary Record Open · 2016
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsnot available
FundersMars Petcare
KeywordsBreedLabrador RetrieverAlbuminInternal medicineWhite blood cellBiologyEndocrinologyAlkaline phosphataseHematologyHemoglobinHematocritPhysiologyAnimal scienceMedicineBiochemistryPathologyEnzyme

Abstract

fetched live from OpenAlex

Breed, sex and age effects on haematological and biochemical variables were investigated in 24 labrador retriever and 25 miniature schnauzer dogs during the first year of life. Blood samples were taken regularly between weeks 8 and 52. White blood cell and red blood cell counts, haemoglobin concentration, haematocrit, mean cell volume, mean cell haemoglobin, mean cell haemoglobin concentration, platelet count as well as total protein, albumin, calcium, phosphate, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, glutamate dehydrogenase, total cholesterol, triglycerides, creatine and urea were evaluated. For all haematological and biochemical parameters, there were significant effects of age on test results. Statistically significant effects for breed and the breed×age interaction on test results were observed for most of the parameters with the exception of haemoglobin. Variations in test results illustrate growth related alterations in body tissue and metabolism leading to dynamic and marked changes in haematological and biochemical parameters, which have to be considered for the interpretation of clinical data obtained from dogs in the first year of life.

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 score0.108
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 communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.070
GPT teacher head0.320
Teacher spread0.250 · 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

Citations37
Published2016
Admission routes1
Has abstractyes

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