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Biological effects of short‐term salmon oil administration, using distinct salmon oil sources in healthy dogs

2012· article· en· W2055462554 on OpenAlexaff
Myriam Hesta, Adronie Verbrugghe, K. E. Gulbrandsen, A. Christophe, Jürgen Zentek, P. Hellweg, G. P. J. Janssens

Bibliographic record

VenueJournal of Small Animal Practice · 2012
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEicosapentaenoic acidFish oilDocosahexaenoic acidPhospholipidPolyunsaturated fatty acidFatty acidMedicineFood scienceAnimal scienceBiologyBiochemistryFisheryFish <Actinopterygii>

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the short-term effects of feeding distinct salmon oil sources in healthy dogs. METHODS: A diet containing chicken fat as major fat source was fed to 17 dogs for 14 days. For the next 14 days, dogs received one of two diets, both with 1% of chicken fat exchanged for 1% salmon oil; Norwegian or Scottish salmon oil, harvested using a distinct procedure. Finally, all dogs were fed chicken fat again for 14 days. RESULTS: Salmon oil increased serum phospholipid total n-3 polyunsaturated fatty acids, eicosapentaenoic and docosahexaenoic acid and decreased total n-6 polyunsaturated fatty acids and n-6:n-3. The phospholipid fatty acid profile returned to initial values within 2 weeks of discontinuing salmon oil administration. Blood coagulation, acute phase response and plasma immunoglobulin concentrations were not affected by salmon oil and no differences were detected for the measured indices between the two salmon oils. CLINICAL SIGNIFICANCE: Low-dose salmon oil administration alters serum phospholipid fatty acid profile within 2 weeks, but without affecting selected immunologic and coagulation indices. Salmon oil sources from different sources and harvested using a distinct procedure did not induce different effects, most probably because of their similar fatty acid profiles.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.392
Threshold uncertainty score0.694

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
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 integrity0.0000.001
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 head0.079
GPT teacher head0.381
Teacher spread0.302 · 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

Citations9
Published2012
Admission routes1
Has abstractyes

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