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Record W2397314844

Serum antibody responses to vaccinal antigens in lean and obese geriatric dogs.

2016· article· en· W2397314844 on OpenAlexaff
John Ellis, Sheryl Gow, Carrie Rhodes, Stacey Lacoste, Lyndsay Kong, Kristyna Musil, Elisabeth Snead

Bibliographic record

VenuePubMed · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCanine distemperTiterMedicineBreedVaccinationCanine parvovirusVirologyAntibodyVirusBiologyImmunologyParvovirusAnimal science
DOInot available

Abstract

fetched live from OpenAlex

The immune responses in control dogs [1 to 4 years of age, body condition score (BCS): 4 to 5 out of 9] were compared to those of aging dogs (based on breed and body size) either categorized as lean (BCS: 4 to 5 out of 9) or obese (BCS: 8 to 9 out of 9). Of interest were the serum titers to the following common agents found in vaccines, canine parainfluenza virus (CPIV), canine parvovirus (CPV), canine distemper virus (CDV), canine respiratory coronavirus (CRCoV), and Bordetella bronchiseptica. There were no statistical differences in the antibodies to CPIV, B. bronchispetica, and CRCoV, among the age/weight categories, nor among the age/weight categories and the time, in days, between the date of sample collection and the date of the last recorded vaccination for CPIV, B. bronchiseptica, CPV, and CDV. For CPV, the control dogs had significantly (P < 0.002) higher serum neutralization (SN) titers than the lean geriatric dogs and the obese geriatric dogs. For CDV SN titers, the only statistically significant (P = 0.01) difference was that the control dogs had higher SN titers than the lean geriatric dogs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.294
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2016
Admission routes1
Has abstractyes

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Same venuePubMed→Same topicVirus-based gene therapy research→French-language works237,207→