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Record W2147735994 · doi:10.1177/104063870802000214

Association of canine obesity with reduced serum levels of C-reactive protein

2008· article· en· W2147735994 on OpenAlexaff
Ângela Patrícia Medeiros Veiga, Christopher A. Price, Simone Tostes de Oliveira, Andrea Pires dos Santos, Rómulo Campos Gaona, Félix González

Bibliographic record

VenueJournal of Veterinary Diagnostic Investigation · 2008
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Medicine and Surgery
Canadian institutionsUniversité de MontréalCegep de Saint Hyacinthe
Fundersnot available
KeywordsInternal medicineFructosamineEndocrinologyC-reactive proteinObesityInsulin resistanceInsulinMedicineTriglycerideOverweightCholesterolInflammation

Abstract

fetched live from OpenAlex

The prevalence of obesity is increasing in dogs as well as in humans. C-reactive protein (CRP) is an important tool for the detection of inflammation and/or early tissue damage and is linked to obesity in humans. The objective of the present study was to determine if serum CRP levels are altered in obese dogs. Fifteen lean (control group) and 16 overweight (obese group) dogs were examined. Blood samples were collected under fasted conditions for serum determination of CRP, glucose, insulin, cholesterol, triglyceride, and fructosamine. Results indicated that obese dogs were insulin resistant because serum insulin and insulin/glucose ratios were higher than in lean dogs (P < or = 0.05). Serum CRP concentrations were lower in obese dogs than in controls (P < or = 0.001). C-reactive protein was negatively correlated with insulin/glucose ratio (R = -0.42) and cholesterol (R = -0.39; P < or = 0.05). Furthermore, levels of cholesterol, triglycerides, and fructosamine were increased in the obese group compared with the control group. Based on these results, it can be postulated that CRP production is inhibited by obesity and insulin resistance in 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 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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.083
GPT teacher head0.295
Teacher spread0.212 · 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.

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

Citations40
Published2008
Admission routes1
Has abstractyes

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